{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries and Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>epoch</th>\n",
       "      <th>sat_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>Vx</th>\n",
       "      <th>Vy</th>\n",
       "      <th>Vz</th>\n",
       "      <th>x_sim</th>\n",
       "      <th>y_sim</th>\n",
       "      <th>z_sim</th>\n",
       "      <th>Vx_sim</th>\n",
       "      <th>Vy_sim</th>\n",
       "      <th>Vz_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-01 00:00:00.000</td>\n",
       "      <td>0</td>\n",
       "      <td>-8855.823863</td>\n",
       "      <td>13117.780146</td>\n",
       "      <td>-20728.353233</td>\n",
       "      <td>-0.908303</td>\n",
       "      <td>-3.808436</td>\n",
       "      <td>-2.022083</td>\n",
       "      <td>-8843.131454</td>\n",
       "      <td>13138.221690</td>\n",
       "      <td>-20741.615306</td>\n",
       "      <td>-0.907527</td>\n",
       "      <td>-3.804930</td>\n",
       "      <td>-2.024133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:46:43.000</td>\n",
       "      <td>0</td>\n",
       "      <td>-10567.672384</td>\n",
       "      <td>1619.746066</td>\n",
       "      <td>-24451.813271</td>\n",
       "      <td>-0.302590</td>\n",
       "      <td>-4.272617</td>\n",
       "      <td>-0.612796</td>\n",
       "      <td>-10555.500066</td>\n",
       "      <td>1649.289367</td>\n",
       "      <td>-24473.089556</td>\n",
       "      <td>-0.303704</td>\n",
       "      <td>-4.269816</td>\n",
       "      <td>-0.616468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 01:33:26.001</td>\n",
       "      <td>0</td>\n",
       "      <td>-10578.684043</td>\n",
       "      <td>-10180.467460</td>\n",
       "      <td>-24238.280949</td>\n",
       "      <td>0.277435</td>\n",
       "      <td>-4.047522</td>\n",
       "      <td>0.723155</td>\n",
       "      <td>-10571.858472</td>\n",
       "      <td>-10145.939908</td>\n",
       "      <td>-24271.169776</td>\n",
       "      <td>0.274880</td>\n",
       "      <td>-4.046788</td>\n",
       "      <td>0.718768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2014-01-01 02:20:09.001</td>\n",
       "      <td>0</td>\n",
       "      <td>-9148.251857</td>\n",
       "      <td>-20651.437460</td>\n",
       "      <td>-20720.381279</td>\n",
       "      <td>0.715600</td>\n",
       "      <td>-3.373762</td>\n",
       "      <td>1.722115</td>\n",
       "      <td>-9149.620794</td>\n",
       "      <td>-20618.200201</td>\n",
       "      <td>-20765.019094</td>\n",
       "      <td>0.712437</td>\n",
       "      <td>-3.375202</td>\n",
       "      <td>1.718306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2014-01-01 03:06:52.002</td>\n",
       "      <td>0</td>\n",
       "      <td>-6719.092336</td>\n",
       "      <td>-28929.061629</td>\n",
       "      <td>-14938.907967</td>\n",
       "      <td>0.992507</td>\n",
       "      <td>-2.519732</td>\n",
       "      <td>2.344703</td>\n",
       "      <td>-6729.358857</td>\n",
       "      <td>-28902.271436</td>\n",
       "      <td>-14992.399986</td>\n",
       "      <td>0.989382</td>\n",
       "      <td>-2.522618</td>\n",
       "      <td>2.342237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                    epoch  sat_id             x             y  \\\n",
       "0   0  2014-01-01 00:00:00.000       0  -8855.823863  13117.780146   \n",
       "1   1  2014-01-01 00:46:43.000       0 -10567.672384   1619.746066   \n",
       "2   2  2014-01-01 01:33:26.001       0 -10578.684043 -10180.467460   \n",
       "3   3  2014-01-01 02:20:09.001       0  -9148.251857 -20651.437460   \n",
       "4   4  2014-01-01 03:06:52.002       0  -6719.092336 -28929.061629   \n",
       "\n",
       "              z        Vx        Vy        Vz         x_sim         y_sim  \\\n",
       "0 -20728.353233 -0.908303 -3.808436 -2.022083  -8843.131454  13138.221690   \n",
       "1 -24451.813271 -0.302590 -4.272617 -0.612796 -10555.500066   1649.289367   \n",
       "2 -24238.280949  0.277435 -4.047522  0.723155 -10571.858472 -10145.939908   \n",
       "3 -20720.381279  0.715600 -3.373762  1.722115  -9149.620794 -20618.200201   \n",
       "4 -14938.907967  0.992507 -2.519732  2.344703  -6729.358857 -28902.271436   \n",
       "\n",
       "          z_sim    Vx_sim    Vy_sim    Vz_sim  \n",
       "0 -20741.615306 -0.907527 -3.804930 -2.024133  \n",
       "1 -24473.089556 -0.303704 -4.269816 -0.616468  \n",
       "2 -24271.169776  0.274880 -4.046788  0.718768  \n",
       "3 -20765.019094  0.712437 -3.375202  1.718306  \n",
       "4 -14992.399986  0.989382 -2.522618  2.342237  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_df = pd.read_csv(\"jan_train.csv\")\n",
    "meta_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(meta_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(503227, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "       104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "       117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "       130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "       143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
       "       156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
       "       169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
       "       195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n",
       "       208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,\n",
       "       221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,\n",
       "       234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,\n",
       "       247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n",
       "       260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,\n",
       "       273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,\n",
       "       286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,\n",
       "       299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,\n",
       "       312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,\n",
       "       325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,\n",
       "       338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350,\n",
       "       351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,\n",
       "       364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376,\n",
       "       377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,\n",
       "       390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402,\n",
       "       403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415,\n",
       "       416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428,\n",
       "       429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441,\n",
       "       442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454,\n",
       "       455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,\n",
       "       468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480,\n",
       "       481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493,\n",
       "       494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506,\n",
       "       507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519,\n",
       "       520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532,\n",
       "       533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545,\n",
       "       546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558,\n",
       "       559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571,\n",
       "       572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584,\n",
       "       585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597,\n",
       "       598, 599], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_df['sat_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 503227 entries, 0 to 503226\n",
      "Data columns (total 15 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   id      503227 non-null  int64  \n",
      " 1   epoch   503227 non-null  object \n",
      " 2   sat_id  503227 non-null  int64  \n",
      " 3   x       503227 non-null  float64\n",
      " 4   y       503227 non-null  float64\n",
      " 5   z       503227 non-null  float64\n",
      " 6   Vx      503227 non-null  float64\n",
      " 7   Vy      503227 non-null  float64\n",
      " 8   Vz      503227 non-null  float64\n",
      " 9   x_sim   503227 non-null  float64\n",
      " 10  y_sim   503227 non-null  float64\n",
      " 11  z_sim   503227 non-null  float64\n",
      " 12  Vx_sim  503227 non-null  float64\n",
      " 13  Vy_sim  503227 non-null  float64\n",
      " 14  Vz_sim  503227 non-null  float64\n",
      "dtypes: float64(12), int64(2), object(1)\n",
      "memory usage: 57.6+ MB\n"
     ]
    }
   ],
   "source": [
    "meta_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis for SAT-0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "filt = (meta_df[\"sat_id\"]==0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          True\n",
       "1          True\n",
       "2          True\n",
       "3          True\n",
       "4          True\n",
       "          ...  \n",
       "503222    False\n",
       "503223    False\n",
       "503224    False\n",
       "503225    False\n",
       "503226    False\n",
       "Name: sat_id, Length: 503227, dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sat0_df = meta_df.loc[filt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>epoch</th>\n",
       "      <th>sat_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>Vx</th>\n",
       "      <th>Vy</th>\n",
       "      <th>Vz</th>\n",
       "      <th>x_sim</th>\n",
       "      <th>y_sim</th>\n",
       "      <th>z_sim</th>\n",
       "      <th>Vx_sim</th>\n",
       "      <th>Vy_sim</th>\n",
       "      <th>Vz_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-01 00:00:00.000</td>\n",
       "      <td>0</td>\n",
       "      <td>-8855.823863</td>\n",
       "      <td>13117.780146</td>\n",
       "      <td>-20728.353233</td>\n",
       "      <td>-0.908303</td>\n",
       "      <td>-3.808436</td>\n",
       "      <td>-2.022083</td>\n",
       "      <td>-8843.131454</td>\n",
       "      <td>13138.221690</td>\n",
       "      <td>-20741.615306</td>\n",
       "      <td>-0.907527</td>\n",
       "      <td>-3.804930</td>\n",
       "      <td>-2.024133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:46:43.000</td>\n",
       "      <td>0</td>\n",
       "      <td>-10567.672384</td>\n",
       "      <td>1619.746066</td>\n",
       "      <td>-24451.813271</td>\n",
       "      <td>-0.302590</td>\n",
       "      <td>-4.272617</td>\n",
       "      <td>-0.612796</td>\n",
       "      <td>-10555.500066</td>\n",
       "      <td>1649.289367</td>\n",
       "      <td>-24473.089556</td>\n",
       "      <td>-0.303704</td>\n",
       "      <td>-4.269816</td>\n",
       "      <td>-0.616468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 01:33:26.001</td>\n",
       "      <td>0</td>\n",
       "      <td>-10578.684043</td>\n",
       "      <td>-10180.467460</td>\n",
       "      <td>-24238.280949</td>\n",
       "      <td>0.277435</td>\n",
       "      <td>-4.047522</td>\n",
       "      <td>0.723155</td>\n",
       "      <td>-10571.858472</td>\n",
       "      <td>-10145.939908</td>\n",
       "      <td>-24271.169776</td>\n",
       "      <td>0.274880</td>\n",
       "      <td>-4.046788</td>\n",
       "      <td>0.718768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2014-01-01 02:20:09.001</td>\n",
       "      <td>0</td>\n",
       "      <td>-9148.251857</td>\n",
       "      <td>-20651.437460</td>\n",
       "      <td>-20720.381279</td>\n",
       "      <td>0.715600</td>\n",
       "      <td>-3.373762</td>\n",
       "      <td>1.722115</td>\n",
       "      <td>-9149.620794</td>\n",
       "      <td>-20618.200201</td>\n",
       "      <td>-20765.019094</td>\n",
       "      <td>0.712437</td>\n",
       "      <td>-3.375202</td>\n",
       "      <td>1.718306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2014-01-01 03:06:52.002</td>\n",
       "      <td>0</td>\n",
       "      <td>-6719.092336</td>\n",
       "      <td>-28929.061629</td>\n",
       "      <td>-14938.907967</td>\n",
       "      <td>0.992507</td>\n",
       "      <td>-2.519732</td>\n",
       "      <td>2.344703</td>\n",
       "      <td>-6729.358857</td>\n",
       "      <td>-28902.271436</td>\n",
       "      <td>-14992.399986</td>\n",
       "      <td>0.989382</td>\n",
       "      <td>-2.522618</td>\n",
       "      <td>2.342237</td>\n",
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      "text/plain": [
       "   id                    epoch  sat_id             x             y  \\\n",
       "0   0  2014-01-01 00:00:00.000       0  -8855.823863  13117.780146   \n",
       "1   1  2014-01-01 00:46:43.000       0 -10567.672384   1619.746066   \n",
       "2   2  2014-01-01 01:33:26.001       0 -10578.684043 -10180.467460   \n",
       "3   3  2014-01-01 02:20:09.001       0  -9148.251857 -20651.437460   \n",
       "4   4  2014-01-01 03:06:52.002       0  -6719.092336 -28929.061629   \n",
       "\n",
       "              z        Vx        Vy        Vz         x_sim         y_sim  \\\n",
       "0 -20728.353233 -0.908303 -3.808436 -2.022083  -8843.131454  13138.221690   \n",
       "1 -24451.813271 -0.302590 -4.272617 -0.612796 -10555.500066   1649.289367   \n",
       "2 -24238.280949  0.277435 -4.047522  0.723155 -10571.858472 -10145.939908   \n",
       "3 -20720.381279  0.715600 -3.373762  1.722115  -9149.620794 -20618.200201   \n",
       "4 -14938.907967  0.992507 -2.519732  2.344703  -6729.358857 -28902.271436   \n",
       "\n",
       "          z_sim    Vx_sim    Vy_sim    Vz_sim  \n",
       "0 -20741.615306 -0.907527 -3.804930 -2.024133  \n",
       "1 -24473.089556 -0.303704 -4.269816 -0.616468  \n",
       "2 -24271.169776  0.274880 -4.046788  0.718768  \n",
       "3 -20765.019094  0.712437 -3.375202  1.718306  \n",
       "4 -14992.399986  0.989382 -2.522618  2.342237  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
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   "source": [
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  {
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   "execution_count": 11,
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   "outputs": [
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       "      <th>737</th>\n",
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       "      <td>2014-01-24 21:03:28.366</td>\n",
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       "      <th>738</th>\n",
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       "      <td>2014-01-24 21:50:11.367</td>\n",
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       "      <td>2014-01-24 22:36:54.367</td>\n",
       "      <td>0</td>\n",
       "      <td>13644.957424</td>\n",
       "      <td>18446.405418</td>\n",
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       "      <td>2.233959</td>\n",
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       "      <td>24593.429195</td>\n",
       "      <td>23717.669835</td>\n",
       "      <td>-1.117669</td>\n",
       "      <td>1.711798</td>\n",
       "      <td>-2.636755</td>\n",
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       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>740</td>\n",
       "      <td>2014-01-24 23:23:37.368</td>\n",
       "      <td>0</td>\n",
       "      <td>10951.688066</td>\n",
       "      <td>24106.659914</td>\n",
       "      <td>24456.597220</td>\n",
       "      <td>-1.093516</td>\n",
       "      <td>1.769369</td>\n",
       "      <td>-2.577266</td>\n",
       "      <td>7135.816766</td>\n",
       "      <td>28430.515190</td>\n",
       "      <td>15552.366754</td>\n",
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      ],
      "text/plain": [
       "      id                    epoch  sat_id             x             y  \\\n",
       "736  736  2014-01-24 20:16:45.366       0  17323.752275  -2929.599994   \n",
       "737  737  2014-01-24 21:03:28.366       0  16803.492692   4510.550397   \n",
       "738  738  2014-01-24 21:50:11.367       0  15589.464016  11758.599852   \n",
       "739  739  2014-01-24 22:36:54.367       0  13644.957424  18446.405418   \n",
       "740  740  2014-01-24 23:23:37.368       0  10951.688066  24106.659914   \n",
       "\n",
       "                z        Vx        Vy        Vz         x_sim         y_sim  \\\n",
       "736  40015.054282 -0.066983  2.651528 -0.240019  16677.210531   5234.016160   \n",
       "737  38575.720092 -0.306878  2.639582 -0.792502  15395.263519  12449.405030   \n",
       "738  35543.718040 -0.561601  2.510341 -1.375365  13380.486475  19062.449796   \n",
       "739  30846.351215 -0.827062  2.233959 -1.978216  10617.516680  24593.429195   \n",
       "740  24456.597220 -1.093516  1.769369 -2.577266   7135.816766  28430.515190   \n",
       "\n",
       "            z_sim    Vx_sim    Vy_sim    Vz_sim  \n",
       "736  38340.904456 -0.330504  2.634282 -0.848806  \n",
       "737  35146.024515 -0.586354  2.491844 -1.435062  \n",
       "738  30278.225301 -0.852275  2.198419 -2.039662  \n",
       "739  23717.669835 -1.117669  1.711798 -2.636755  \n",
       "740  15552.366754 -1.359856  0.981096 -3.172296  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "sat0_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "Empty DataFrame\n",
       "Columns: [id, epoch, sat_id, x, y, z, Vx, Vy, Vz, x_sim, y_sim, z_sim, Vx_sim, Vy_sim, Vz_sim]\n",
       "Index: []"
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    "sat0_df.loc[sat0_df[\"sat_id\"]==1]"
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   "cell_type": "code",
   "execution_count": 13,
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   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 741 entries, 0 to 740\n",
      "Data columns (total 15 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   id      741 non-null    int64  \n",
      " 1   epoch   741 non-null    object \n",
      " 2   sat_id  741 non-null    int64  \n",
      " 3   x       741 non-null    float64\n",
      " 4   y       741 non-null    float64\n",
      " 5   z       741 non-null    float64\n",
      " 6   Vx      741 non-null    float64\n",
      " 7   Vy      741 non-null    float64\n",
      " 8   Vz      741 non-null    float64\n",
      " 9   x_sim   741 non-null    float64\n",
      " 10  y_sim   741 non-null    float64\n",
      " 11  z_sim   741 non-null    float64\n",
      " 12  Vx_sim  741 non-null    float64\n",
      " 13  Vy_sim  741 non-null    float64\n",
      " 14  Vz_sim  741 non-null    float64\n",
      "dtypes: float64(12), int64(2), object(1)\n",
      "memory usage: 92.6+ KB\n"
     ]
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   "source": [
    "sat0_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sat0_df_univ = sat0_df[[\"epoch\", \"x\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-01-01 03:53:35.002</td>\n",
       "      <td>-3708.453525</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-01-01 04:40:18.003</td>\n",
       "      <td>-437.699227</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-01-01 05:27:01.003</td>\n",
       "      <td>2863.147037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-01-01 06:13:44.004</td>\n",
       "      <td>6031.593902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-01-01 07:00:27.004</td>\n",
       "      <td>8950.655291</td>\n",
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      "text/plain": [
       "                     epoch             x\n",
       "0  2014-01-01 00:00:00.000  -8855.823863\n",
       "1  2014-01-01 00:46:43.000 -10567.672384\n",
       "2  2014-01-01 01:33:26.001 -10578.684043\n",
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       "5  2014-01-01 03:53:35.002  -3708.453525\n",
       "6  2014-01-01 04:40:18.003   -437.699227\n",
       "7  2014-01-01 05:27:01.003   2863.147037\n",
       "8  2014-01-01 06:13:44.004   6031.593902\n",
       "9  2014-01-01 07:00:27.004   8950.655291"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat0_df_univ.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 741 entries, 0 to 740\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   epoch   741 non-null    object \n",
      " 1   x       741 non-null    float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 17.4+ KB\n"
     ]
    }
   ],
   "source": [
    "sat0_df_univ.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "sat0_df_univ[\"epoch\"] = pd.to_datetime(sat0_df_univ[\"epoch\"])"
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                    epoch             x\n",
       "0 2014-01-01 00:00:00.000  -8855.823863\n",
       "1 2014-01-01 00:46:43.000 -10567.672384\n",
       "2 2014-01-01 01:33:26.001 -10578.684043\n",
       "3 2014-01-01 02:20:09.001  -9148.251857\n",
       "4 2014-01-01 03:06:52.002  -6719.092336"
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     },
     "execution_count": 18,
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   "source": [
    "sat0_df_univ.head()"
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  {
   "cell_type": "code",
   "execution_count": 19,
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   "outputs": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>2014-01-24 20:16:45.366</td>\n",
       "      <td>17323.752275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>2014-01-24 21:03:28.366</td>\n",
       "      <td>16803.492692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>2014-01-24 21:50:11.367</td>\n",
       "      <td>15589.464016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>2014-01-24 22:36:54.367</td>\n",
       "      <td>13644.957424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2014-01-24 23:23:37.368</td>\n",
       "      <td>10951.688066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      epoch             x\n",
       "736 2014-01-24 20:16:45.366  17323.752275\n",
       "737 2014-01-24 21:03:28.366  16803.492692\n",
       "738 2014-01-24 21:50:11.367  15589.464016\n",
       "739 2014-01-24 22:36:54.367  13644.957424\n",
       "740 2014-01-24 23:23:37.368  10951.688066"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat0_df_univ.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 741 entries, 0 to 740\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   epoch   741 non-null    datetime64[ns]\n",
      " 1   x       741 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(1)\n",
      "memory usage: 17.4 KB\n"
     ]
    }
   ],
   "source": [
    "sat0_df_univ.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('23 days 23:23:37.368000')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat0_df_univ['epoch'].max() - sat0_df_univ['epoch'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "figure(figsize=(16, 10), dpi=80)\n",
    "\n",
    "plt.plot(sat0_df_univ[\"epoch\"], sat0_df_univ[\"x\"])\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"x co-ordinates\")\n",
    "plt.title(\"Position(x) of Satellite\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      741.000000\n",
       "mean      4912.520136\n",
       "std       9851.147157\n",
       "min     -10716.106467\n",
       "25%      -5156.221079\n",
       "50%       7005.209383\n",
       "75%      15173.743957\n",
       "max      17325.111650\n",
       "Name: x, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pos = sat0_df_univ[\"x\"]\n",
    "x_pos.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = x_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(741,)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examine stationarity of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dickey-Fuller Test\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "adf, pvalue, usedlag_, nobs_, critical_values_, icbest_ = adfuller(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.002473777001334866\n"
     ]
    }
   ],
   "source": [
    "print(pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autocorrelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2594b71c588>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import autocorrelation_plot\n",
    "autocorrelation_plot(data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(741,)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(741, 1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [ -7013.29465297],\n",
       "       [ -4050.50802982],\n",
       "       [  -801.21971246],\n",
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       "       [  5683.48434885],\n",
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       "       [ 13470.78767264],\n",
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       "       [ 16768.42056072],\n",
       "       [ 15526.20459684],\n",
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       "       [  7396.21904321],\n",
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       "       [ -5265.58544295],\n",
       "       [ -8633.33235069],\n",
       "       [-10512.75737797],\n",
       "       [-10684.99688575],\n",
       "       [ -9382.24521946],\n",
       "       [ -7040.68741648],\n",
       "       [ -4081.919352  ],\n",
       "       [  -834.15511654],\n",
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       "       [  8604.32613875],\n",
       "       [ 11230.83326372],\n",
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       "       [ 15236.37023233],\n",
       "       [ 16503.47379068],\n",
       "       [ 17213.56317182],\n",
       "       [ 17319.2761261 ],\n",
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       "       [ 15535.66351437],\n",
       "       [ 13567.00729322],\n",
       "       [ 10851.49072094],\n",
       "       [  7414.36484044],\n",
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       "       [ -1020.50596048],\n",
       "       [ -5249.93801834],\n",
       "       [ -8623.56305224],\n",
       "       [-10510.40208865],\n",
       "       [-10689.72986389],\n",
       "       [ -9392.50384022],\n",
       "       [ -7054.63794693],\n",
       "       [ -4097.95933584],\n",
       "       [  -851.01830491],\n",
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       "       [  8590.03408586],\n",
       "       [ 11218.53076838],\n",
       "       [ 13451.37594642],\n",
       "       [ 15229.15832159],\n",
       "       [ 16499.23562657],\n",
       "       [ 17212.5117812 ],\n",
       "       [ 17321.56999468],\n",
       "       [ 16780.19539779],\n",
       "       [ 15544.84986328],\n",
       "       [ 13579.53614609],\n",
       "       [ 10867.06241113],\n",
       "       [  7432.37057795],\n",
       "       [  3386.66981776],\n",
       "       [ -1001.66710986],\n",
       "       [ -5234.17851604],\n",
       "       [ -8613.61718509],\n",
       "       [-10507.87720902],\n",
       "       [-10694.37189183],\n",
       "       [ -9402.78469647],\n",
       "       [ -7068.7232766 ],\n",
       "       [ -4114.22942124],\n",
       "       [  -868.18617134],\n",
       "       [  2432.70821675],\n",
       "       [  5620.81317535],\n",
       "       [  8575.30834691],\n",
       "       [ 11205.77067088],\n",
       "       [ 13440.9796868 ],\n",
       "       [ 15221.45301403],\n",
       "       [ 16494.48904243],\n",
       "       [ 17210.93781244],\n",
       "       [ 17323.32797939],\n",
       "       [ 16785.3820343 ],\n",
       "       [ 15553.48029202],\n",
       "       [ 13591.50745486],\n",
       "       [ 10882.08728601],\n",
       "       [  7449.8618173 ],\n",
       "       [  3405.55619997],\n",
       "       [  -983.16550795],\n",
       "       [ -5218.60727537],\n",
       "       [ -8603.71544575],\n",
       "       [-10505.31908821],\n",
       "       [-10698.99725163],\n",
       "       [ -9413.13455556],\n",
       "       [ -7082.99134909],\n",
       "       [ -4130.79507587],\n",
       "       [  -885.74767981],\n",
       "       [  2415.1647474 ],\n",
       "       [  5604.07908113],\n",
       "       [  8560.01029704],\n",
       "       [ 11192.41335171],\n",
       "       [ 13429.97527204],\n",
       "       [ 15213.1406085 ],\n",
       "       [ 16489.1465778 ],\n",
       "       [ 17208.78770396],\n",
       "       [ 17324.53673715],\n",
       "       [ 16790.05255704],\n",
       "       [ 15561.6337162 ],\n",
       "       [ 13603.04783687],\n",
       "       [ 10896.73702605],\n",
       "       [  7467.04840978],\n",
       "       [  3424.23005291],\n",
       "       [  -964.75847734],\n",
       "       [ -5202.9979614 ],\n",
       "       [ -8593.66977285],\n",
       "       [-10502.59201448],\n",
       "       [-10703.5234442 ],\n",
       "       [ -9423.51488744],\n",
       "       [ -7097.43595929],\n",
       "       [ -4147.67284225],\n",
       "       [  -903.73542546],\n",
       "       [  2397.10444665],\n",
       "       [  5586.75979739],\n",
       "       [  8544.0784101 ],\n",
       "       [ 11178.3906783 ],\n",
       "       [ 13418.28882869],\n",
       "       [ 15204.14244166],\n",
       "       [ 16483.12597161],\n",
       "       [ 17205.97711959],\n",
       "       [ 17325.11165009],\n",
       "       [ 16794.12403786],\n",
       "       [ 15569.23083032],\n",
       "       [ 13614.08238579],\n",
       "       [ 10910.94077977],\n",
       "       [  7483.86201747],\n",
       "       [  3442.6242345 ],\n",
       "       [  -946.51071815],\n",
       "       [ -5187.40511535],\n",
       "       [ -8583.5078109 ],\n",
       "       [-10499.67892642],\n",
       "       [-10707.8844849 ],\n",
       "       [ -9433.82220938],\n",
       "       [ -7111.93344427],\n",
       "       [ -4164.73414467],\n",
       "       [  -922.02641489],\n",
       "       [  2378.63897757],\n",
       "       [  5568.95305648],\n",
       "       [  8527.59613809],\n",
       "       [ 11163.77281224],\n",
       "       [ 13405.97886642],\n",
       "       [ 15194.50711783],\n",
       "       [ 16476.46747261],\n",
       "       [ 17202.53907764],\n",
       "       [ 17325.07911179],\n",
       "       [ 16797.61628422],\n",
       "       [ 15576.28373394],\n",
       "       [ 13624.61522083],\n",
       "       [ 10924.69389434],\n",
       "       [  7500.28816538],\n",
       "       [  3460.71154008],\n",
       "       [  -928.46446879],\n",
       "       [ -5171.88388635],\n",
       "       [ -8573.2897769 ],\n",
       "       [-10496.63432643],\n",
       "       [-10712.1202861 ],\n",
       "       [ -9444.07847106],\n",
       "       [ -7126.48956372],\n",
       "       [ -4181.97216157],\n",
       "       [  -940.60443408],\n",
       "       [  2359.79167538],\n",
       "       [  5550.68848792],\n",
       "       [  8510.59974714],\n",
       "       [ 11148.60409909],\n",
       "       [ 13393.10033302],\n",
       "       [ 15184.30375362],\n",
       "       [ 16469.25878866],\n",
       "       [ 17198.58486307],\n",
       "       [ 17324.57907547],\n",
       "       [ 16800.70241239],\n",
       "       [ 15583.0023144 ],\n",
       "       [ 13634.89236717],\n",
       "       [ 10938.27324496],\n",
       "       [  7516.62215488],\n",
       "       [  3478.78622095],\n",
       "       [  -910.35321561],\n",
       "       [ -5156.22107937],\n",
       "       [ -8562.86052914],\n",
       "       [-10493.33580526],\n",
       "       [-10716.10646746],\n",
       "       [ -9454.13563938],\n",
       "       [ -7140.92637698],\n",
       "       [ -4199.18210335],\n",
       "       [  -959.24469254],\n",
       "       [  2340.80004735],\n",
       "       [  5532.2094063 ],\n",
       "       [  8493.33227336],\n",
       "       [ 11133.12192685],\n",
       "       [ 13379.88005173],\n",
       "       [ 15173.74395718],\n",
       "       [ 16461.69188016],\n",
       "       [ 17194.28266679],\n",
       "       [ 17323.75227463],\n",
       "       [ 16803.49269223],\n",
       "       [ 15589.46401638],\n",
       "       [ 13644.9574239 ],\n",
       "       [ 10951.688066  ]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split data into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>2014-01-18 04:39:50.264</td>\n",
       "      <td>-10680.363547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>2014-01-18 05:26:33.265</td>\n",
       "      <td>-9372.150198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>2014-01-18 06:13:16.265</td>\n",
       "      <td>-7026.975763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>2014-01-18 06:59:59.265</td>\n",
       "      <td>-4066.186100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>2014-01-18 07:46:42.266</td>\n",
       "      <td>-817.652541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>2014-01-24 20:16:45.366</td>\n",
       "      <td>17323.752275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>2014-01-24 21:03:28.366</td>\n",
       "      <td>16803.492692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>2014-01-24 21:50:11.367</td>\n",
       "      <td>15589.464016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>2014-01-24 22:36:54.367</td>\n",
       "      <td>13644.957424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2014-01-24 23:23:37.368</td>\n",
       "      <td>10951.688066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      epoch             x\n",
       "531 2014-01-18 04:39:50.264 -10680.363547\n",
       "532 2014-01-18 05:26:33.265  -9372.150198\n",
       "533 2014-01-18 06:13:16.265  -7026.975763\n",
       "534 2014-01-18 06:59:59.265  -4066.186100\n",
       "535 2014-01-18 07:46:42.266   -817.652541\n",
       "..                      ...           ...\n",
       "736 2014-01-24 20:16:45.366  17323.752275\n",
       "737 2014-01-24 21:03:28.366  16803.492692\n",
       "738 2014-01-24 21:50:11.367  15589.464016\n",
       "739 2014-01-24 22:36:54.367  13644.957424\n",
       "740 2014-01-24 23:23:37.368  10951.688066\n",
       "\n",
       "[210 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat0_df_univ.tail(210)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "741"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "521"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "741-220"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_len = 521\n",
    "test_len = 220"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data[:train_len]\n",
    "test = data[train_len:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "741\n"
     ]
    }
   ],
   "source": [
    "print(len(train)+len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((521, 1), (220, 1))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scale down the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_scaled = scaler.fit_transform(train)\n",
    "test_scaled = scaler.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.06501939],\n",
       "       [0.00385889],\n",
       "       [0.00346547],\n",
       "       [0.05457159],\n",
       "       [0.14136   ],\n",
       "       [0.24892335],\n",
       "       [0.36578004],\n",
       "       [0.48371185],\n",
       "       [0.59691333],\n",
       "       [0.70120482]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scaled[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.98028993],\n",
       "       [0.93578473],\n",
       "       [0.86521155],\n",
       "       [0.76797691],\n",
       "       [0.64500574],\n",
       "       [0.50032533],\n",
       "       [0.34360168],\n",
       "       [0.19272391],\n",
       "       [0.07261526],\n",
       "       [0.00573401]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scaled[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(521, 220)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_scaled), len(test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create sequences using Keras TimeSeries Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of samples in the original training data =  521\n",
      "Total number of samples in the generated data =  501\n"
     ]
    }
   ],
   "source": [
    "#Sequence size has an impact on prediction\n",
    "seq_size = 20  ## number of steps (lookback)\n",
    "n_features = 1 ## number of features. This dataset is univariate so it is 1\n",
    "train_generator = TimeseriesGenerator(train_scaled, train_scaled, length = seq_size, batch_size=1)\n",
    "print(\"Total number of samples in the original training data = \", len(train_scaled)) # 521\n",
    "print(\"Total number of samples in the generated data = \", len(train_generator)) # 501 with seq_size=20\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check data shape from generator\n",
    "x, y = train_generator[10]  #Check train_generator\n",
    "#Takes 7 days as x and 8th day as y (for seq_size=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[0.79345838],\n",
       "         [0.87120861],\n",
       "         [0.93239418],\n",
       "         [0.97518705],\n",
       "         [0.99788257],\n",
       "         [0.99883809],\n",
       "         [0.97646269],\n",
       "         [0.92927972],\n",
       "         [0.85611654],\n",
       "         [0.75653331],\n",
       "         [0.63169842],\n",
       "         [0.48601917],\n",
       "         [0.3296941 ],\n",
       "         [0.1811459 ],\n",
       "         [0.06535773],\n",
       "         [0.00393308],\n",
       "         [0.00329264],\n",
       "         [0.05420706],\n",
       "         [0.14086607],\n",
       "         [0.24835446]]]),\n",
       " array([[0.36518004]]))"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of samples in the original training data =  220\n",
      "Total number of samples in the generated data =  200\n"
     ]
    }
   ],
   "source": [
    "#Also generate test data\n",
    "test_generator = TimeseriesGenerator(test_scaled, test_scaled, length=seq_size, batch_size=1)\n",
    "print(\"Total number of samples in the original training data = \", len(test_scaled)) \n",
    "print(\"Total number of samples in the generated data = \", len(test_generator)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[ 9.80289925e-01],\n",
       "         [ 9.35784734e-01],\n",
       "         [ 8.65211546e-01],\n",
       "         [ 7.67976914e-01],\n",
       "         [ 6.45005743e-01],\n",
       "         [ 5.00325335e-01],\n",
       "         [ 3.43601684e-01],\n",
       "         [ 1.92723909e-01],\n",
       "         [ 7.26152591e-02],\n",
       "         [ 5.73401203e-03],\n",
       "         [-1.67310932e-04],\n",
       "         [ 4.65722085e-02],\n",
       "         [ 1.30360014e-01],\n",
       "         [ 2.36142367e-01],\n",
       "         [ 3.52205162e-01],\n",
       "         [ 4.70122962e-01],\n",
       "         [ 5.83929873e-01],\n",
       "         [ 6.89326390e-01],\n",
       "         [ 7.83094460e-01],\n",
       "         [ 8.62698981e-01]]]),\n",
       " array([[0.92602221]]))"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check data shape from generator\n",
    "x, y = test_generator[0]\n",
    "x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(150, activation='relu', return_sequences=True, input_shape=(seq_size, n_features)))\n",
    "model.add(LSTM(64, activation='relu'))\n",
    "model.add(Dense(64))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 20, 150)           91200     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 64)                55040     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 150,465\n",
      "Trainable params: 150,465\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:1940: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "10/10 [==============================] - 4s 124ms/step - loss: 0.2866 - val_loss: 0.1766\n",
      "Epoch 2/50\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.1843 - val_loss: 0.1478\n",
      "Epoch 3/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.1512 - val_loss: 0.1331\n",
      "Epoch 4/50\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.1257 - val_loss: 0.1587\n",
      "Epoch 5/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.1536 - val_loss: 0.1657\n",
      "Epoch 6/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.1464 - val_loss: 0.0983\n",
      "Epoch 7/50\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.0581 - val_loss: 0.0925\n",
      "Epoch 8/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.0695 - val_loss: 0.0605\n",
      "Epoch 9/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.0873 - val_loss: 0.0229\n",
      "Epoch 10/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.0348 - val_loss: 0.0126\n",
      "Epoch 11/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.0158 - val_loss: 0.0216\n",
      "Epoch 12/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.0295 - val_loss: 0.0471\n",
      "Epoch 13/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.0220 - val_loss: 0.0122\n",
      "Epoch 14/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.0199 - val_loss: 0.0253\n",
      "Epoch 15/50\n",
      "10/10 [==============================] - 1s 78ms/step - loss: 0.0142 - val_loss: 0.0167\n",
      "Epoch 16/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.0170 - val_loss: 0.0104\n",
      "Epoch 17/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.0150 - val_loss: 0.0020\n",
      "Epoch 18/50\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.0243 - val_loss: 0.0639\n",
      "Epoch 19/50\n",
      "10/10 [==============================] - 1s 78ms/step - loss: 0.0200 - val_loss: 0.0163\n",
      "Epoch 20/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.0100 - val_loss: 0.0078\n",
      "Epoch 21/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.0048 - val_loss: 0.0086\n",
      "Epoch 22/50\n",
      "10/10 [==============================] - 1s 78ms/step - loss: 0.0074 - val_loss: 0.0068\n",
      "Epoch 23/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.0034 - val_loss: 0.0040\n",
      "Epoch 24/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.0040 - val_loss: 0.0022\n",
      "Epoch 25/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.0025 - val_loss: 0.0012\n",
      "Epoch 26/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.0011 - val_loss: 0.0010\n",
      "Epoch 27/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.0015 - val_loss: 6.7347e-04\n",
      "Epoch 28/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 8.5693e-04 - val_loss: 0.0012\n",
      "Epoch 29/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.0012 - val_loss: 4.5739e-04\n",
      "Epoch 30/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.0011 - val_loss: 8.8577e-04\n",
      "Epoch 31/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 7.7688e-04 - val_loss: 6.9165e-04\n",
      "Epoch 32/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.0015 - val_loss: 0.0010\n",
      "Epoch 33/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.0037 - val_loss: 0.0025\n",
      "Epoch 34/50\n",
      "10/10 [==============================] - 1s 78ms/step - loss: 0.0014 - val_loss: 8.7927e-04\n",
      "Epoch 35/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 7.4987e-04 - val_loss: 0.0021\n",
      "Epoch 36/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 8.7473e-04 - val_loss: 0.0016\n",
      "Epoch 37/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 5.3923e-04 - val_loss: 9.7121e-04\n",
      "Epoch 38/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 8.5226e-04 - val_loss: 2.5786e-04\n",
      "Epoch 39/50\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 3.1053e-04 - val_loss: 0.0010\n",
      "Epoch 40/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.0014 - val_loss: 4.3379e-04\n",
      "Epoch 41/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 6.0403e-04 - val_loss: 2.7891e-04\n",
      "Epoch 42/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 5.3359e-04 - val_loss: 5.1127e-04\n",
      "Epoch 43/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 5.3565e-04 - val_loss: 2.1596e-04\n",
      "Epoch 44/50\n",
      "10/10 [==============================] - 1s 78ms/step - loss: 6.9663e-04 - val_loss: 0.0011\n",
      "Epoch 45/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 4.4518e-04 - val_loss: 1.4410e-04\n",
      "Epoch 46/50\n",
      "10/10 [==============================] - 1s 78ms/step - loss: 1.8237e-04 - val_loss: 2.1125e-04\n",
      "Epoch 47/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 8.5413e-04 - val_loss: 0.0014\n",
      "Epoch 48/50\n",
      "10/10 [==============================] - 1s 78ms/step - loss: 9.6498e-04 - val_loss: 2.3738e-04\n",
      "Epoch 49/50\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 3.3572e-04 - val_loss: 3.2898e-04\n",
      "Epoch 50/50\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 7.3317e-04 - val_loss: 8.9889e-04\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_generator, \n",
    "                              validation_data=test_generator, \n",
    "                              epochs=50, steps_per_epoch=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the training and validation accuracy and loss at each epoch\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(loss) + 1)\n",
    "plt.plot(epochs, loss, 'y', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_predictions = model.predict(train_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = model.predict(test_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(501, 200)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_predictions), len(test_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Estimate model performance\n",
    "#SInce we used minmaxscaler we can now use scaler.inverse_transform\n",
    "#to invert the transformation.\n",
    "\n",
    "train_predictions = scaler.inverse_transform(train_predictions)\n",
    "#trainY_inverse = scaler.inverse_transform([trainY])\n",
    "test_predictions = scaler.inverse_transform(test_predictions)\n",
    "#testY_inverse = scaler.inverse_transform([testY])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "\n",
    "# shift train predictions for plotting\n",
    "#we must shift the predictions so that they align on the x-axis with the original dataset. \n",
    "trainPredictPlot = np.empty_like(data)\n",
    "trainPredictPlot[:, :] = np.nan\n",
    "trainPredictPlot[seq_size:len(train_predictions)+seq_size, :] = train_predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shift test predictions for plotting\n",
    "testPredictPlot = np.empty_like(data)\n",
    "testPredictPlot[:, :] = np.nan\n",
    "testPredictPlot[len(train_predictions)+(seq_size*2):len(data), :] = test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HQVBiXKdudMCORMH4aJ0jzStG49Y1OXBm/ptvCIMz42R1owMm+4PklLjbAyXrrMRZyu+w3mn2YYEbx0vcO5NhuMyhffN4gWdXS0EJUPu5PXu8mCXv9ZVcXvPZ37p2gKcP443hdG4YgPZ5t8xBLsekHsYhPqMGD1IFwt2zFNdXC1xfLQDIHbh3zxI8e7zA00fV/RFSF9jePU1w43iBp+q8j9ZSeav35NNH1T48FshrZuSurxZ46rBar8QdKI+THFmhcX21wLUDubzme/Hs8bK5/+RUwBa4C0qOl1XecwElwL0OeDg5iHCeFSIzXIbZMes9F3inA20nv8krJFVvwM5qgdVCfr3PrhZYLUMxi2jjknbrZIlFFIit1zxnpsiXGgUw+/vp16t3r9g+nCVYRAFu1DWJVN77ZxmeOYpxUr8bJO8ju4zwYGePY50VOE3yujioDlsRRuOscp26cdIethKH+N1OEWq+UKcC3QPzsrrROWwlbq82h3g3r8QLoCnGT5Y4XlYXSEocYN0i/3gZodTAOpNlolbLSOzlau6OePZ4UeeVk9R0814IrdeA35vHSxwtIlwISTCBao+fPoqb75vcXlTg4bC+qFTqs6vyVs8DIHcw3j2tGI3Voi5uBfZBa407Z2m1XsG8SV4xys8eL7Cq9/cs4ee9XzPK1fNr8gq8z07bTr7JK7EP9zqg5MiAEoH1dovxI/O5Cexvtxg/WkTQGlgLyPnMep9brPFp6g7O00Kk2WQ+t9vBfXxW8EmRxthG3vxP8PnRCziTejeYfXj0Drwi+oTIdwJoweStF1+PPxN/XOydc/8shVIaz33o5/GFwcfF9vfeWYprywA3//hV+Fz1MbFLW++dZ/jUI43n3vbP8dnqBbH9vXuW4DMOU/yp3/1WfIZ6UWwfLis82NnjMF+ek4OouXVd4vBq88aixYGhRa8dxp31CnQsayB27TDCYb3eC+H1Hgrur+mCP3UYN8WBxIvFdKufOopxtJTb3431LkJkhRaR85m8zxwtcLSQ6wCa+bLrqyqv2OF1niJQ1XO2Woaina97NVOyiAJEgZIrEM7SpnMLyBSLQD2jsWqLfInvRZfRWAk2AU6TilGuinGTV7bIlwSTXXnRkeD7t1/kV3nlQNSNLuiTYKpPW8aoAX0C673TbYYIgtS7Zym+WH0If/Fn/hy+6WN/B0VZNhcFc+LeWYovU+/Cl/7UX8A33ftGQYYgxV8Nfg+f8xP/Mb7p9FvF3g33zlJ8ZfB63PzJ/wz/KPsu0fX+7fiXcfTTX4X/HT8kyux80/LfIvi5r8U/WvyoXEPoLMW3Rz+Axa//b/j70U8L7m+Cf17+Xzh5yz/B14a/KHYOPThL8H3nfx/P/MEP4KvD14gxiJcVHuzscZgvz2rZATsCD6j58hwvo/YQl8hbv0RWi1D0EDcH1WoRiRZfp80+hE2xKHEomkJgtQhxFMsVSd19kNzf085zZooOCTBpnrPVMsJqEWGdlSKuf+dJAaWAwzjE0SIUWStQfUarZQSlFA4XkVjeKneBkxr4SgK/0yTH8UGEgziAUnJMyWma4+QgEi3yz9McWm82LSSKZtO0eKrTZJEomo0G/6nDuMNw8df7sNNckGR2mrxH3byS65Vldkwz5OnDWJTZedjkXTTPmcTz++A8xf8Q/TIA4Lh4iGNciHzf7p9n+NvRLwEAbuafQFEUIs2m++cpvi76RQDAZ+TPizE7988zfH1crfcLiveLNpv+x7DK+5n4uNx6TxN8NV4NALipHoo1ms5OT/GfFr8JAHgqWIutNzh7CV+S/yEAYKXWYo2mp9fP43bxAgDgGBdi672s8GBnj8MU4xvFrYC0xhysR4uwBVEieYs6b9QpOmSLZknG6LzZh7aou8hk8zbFgcA+mP/mjc9NMG/3OZN4EZocBpQAEJGGnaU5VosKlFTMTi4iJzlLigb0rgQZI6Bas9lbKUmf1hrnadHsxWoRiXxuF1kBrc33WA6UmP/mo42mhQyIAqrPTJKpPtv4Xkg2Ldomy2Es3xQ6XkaizI7Zh+NlJMrsNE2hjbxy+7tahqLMzlma4zbuNb+uCmeZ5/e2ut/8+joeiz1n3bw6W4s0m86SHLfqvDkinCcy83EX6wTX8RAAEKIUY0qQPMIBKrZvhbUYU3KQ3mn++bo6FQMlq07em8Fjke/EaZLjJh40v76lHoiBvssKD3b2OJpDvFPkS3SbzzuHzJGgLGx4vbLFjChjlLbFgSSzc9opDiRBSXe9kh1sk/doKQuizpICh3GIMFDizKTJtzKafIHZpYrZqfJKsi9AtWbp3EleMWWGdThchLJM6rL67A7iQJRRXi3b77FIEWqaLN3vhUQRmrTfi9VcoE9QvtVthkiCku4+SIKo8+7zsBTM2z3fJPMmBZ7rgIebeCD2fdvIqx4IfW45bqq2wL2pHoo8Z3lyjms4AwBEyBFnj0QMIBbpfYSo3uNP6UdIEhnnw6P0bvPP17XMZ6a1xipr895U90XeORdZgVudz+w59UAE9F2kRQ/syAD1ywwPdvY4WmYnFO0sbjJGgp2vTpEkWsxsgDO5YmaI4RIBfckAeJBcb2d/peRmiyhAHAayxUwtCwMgzHAVnbyyMsEm7yISm11K8xJZoZu9PVpEIt+Ltmg2wE9mzqgtmg3LFYkX+dXzpoQZmLD5/ES+F11mR3DGaAj0SRQ0Z8lAkS8qT45E5XFnXdAneF7MBfrO0m3wIDGUvk4SPKsedfI+EPncVPIQS7Ssy03IFM4HyZ2NX9/AAxHGvstoAMBBek+EsT/J27yHWEMnj9k511mJWx3w8Ix+iIuE7+R63nvGbuC+yDNWPbsPm1/fCh54GZuPJyeGGBiZoqN72MrJt4bkZiKypU5xcDgTw3UoypQMyA+FOoBRoLAIA3HQZ4oCaWanYTNi2f09atYrK1tqgIPg7FL3+wZUeyz7vWhBlEzeduYMqBgj0Rmuzmcn1XE3+ZZRgEDJMyWzyfkEXdOGZK6y65Vtup0PgUlJpnohC/ry9RmuqQvg2qcDqEGJwHrj9V0E0MBTn1HlxUORz+0wean6h6c+s8qrZArcBpQ8bfLKMEbHhil55rMAVCCKawCR5iWulzV4uP45AIBVh+mhxgYouf65CFFikTxw/0u75E0K3KylfHj283GCc+TJmUjehjG68YW4ARlAfZnhwc4ex2mnyA8DhUUUiBQd3YF0WQZmLo17V9stV3T0B+gBqSI/R6CAgzgQn00wA/QNsyNQ3G4wJZKfW2dO5Uiw436eDK1XKG+9XjNLISkTbOaBlpEQA7PJ7BwtQtnO+LLL7Aiut/PZSXXcgeo9aWaXZOV8EQ5jQeOOwWaIcJE/B6O8mEd+eLiQNYg5T3IcxAHCQDXvX4nu+HJdg4dP+bMAgBtCw+4NKLn9ZwDIMTtNUV+v96Z6KMLsnBhQcrvOK8AYFaXG0+X9zbzqAfs9eZEWuKk28z5T3mMz9udp0YKden9X2R3Hv0HIWz8PR6lE3nxjvQvkCJL77n/pCQsPdvY4zjtys+r/ZbT+3bzGCleqyAc2D0UZeUZn4F9wFsgUHUexPGNkhsYlLa2HGBgpkGqeMWkmyqxXWk6ytQ/M5yHNS6RF2Rb4kh33ZBM8HC7CZt6GE92i2eSXLJqbPV6GMt/j/noXoUhR12VKgGp/ZeV8VbPpMA5lQF+nyBdlYDrM2RwzRptNIZn9XYQBFlEg3GQpZnmfbYESPBRZb8OU3G5BicTn1si3uuCBud6y1Hi6MCDqS8Xynqd5KwvrgjPmPpylectobOTlrfcs7Qz81/v7VHGf/U7fACX1cybDRFUzO1l41DBcR2s+iLrM8GBnj6M7qwIY2YfcAP2mrEauSDpeylrhblhaC85+nCUFjhYhgkCJDgqfJnkjy2gsa4UMCraLcaGB/2Vb2AIyILU7syNlcZ4XJdZZ2WEHZED1RfPs9uVx8uBBiu3rznCZvBcCt8X3wZlxvOPG1nqFXOm6MlfZvJvv35U06FuGiOtiX2a2pmPUsBRsCtVM9TIKOnIzofdv7z0pJedr88q9148NKLnxBSiDhZiRwElumJIOs8Ncr9YaT+W1c5wp8gXA2TrfZjRuKf7Q/3laNA5vuP0lVV7cFwFRzaxKnVfic6vW+wCFioCbr6jWq/jrPUsL3FQPsV4808gaT/J77He6mdlJD24Ax88BAI5zPoi6zPBgZ4+jr8mXcls6T7ZBlIwsoS1mlFI4kuqEpgWWUYAoDBo5idihaDqAsbDMqpFDCbrSJR3wIGhZ212v1GxNWda2yE0hLrNeY5HeAAch0GcaAEcd1gGQk9UA2AZo3K5l594lk1fitviuMYrJK3Fb/PZ6hZjqHrNzJPWe7DA7Jr8ks3PUYewlZ4wO4xCLsL68VpipbhkjmfWa943k/Wkb7zNBhUEDSk5uIz+6JSY3a0DJ05+JbPFUNbPDXG9alLiBGjzceiVKFdWyMP47p5kp6TJRzH04S6piXEMBz31xtWyh9d7CfSThcTMLdEvgcztPalCyvAGc3AZQg0l2061ijCpQcqvKq/gGEOdpgRvqAbLDWy3Yye6KGEBcVniws8fRH+hdiQ8Kt8WBTN4CcaiwjMxgs8zFjKedIr+xwhVhYNpi/FBaZtV0g2WZne0in5dXa12vV7bDakBJHzxwP7c+UJcCfedbBb6cccc2eJAxl+havW/m5R+M3byrRYii1EgLrs59EzxIubz15XErIeOD/qyVFIg6GwBRUi6Fxurd3EElJTczTEnD2Au5CTYNgObSVhl58uFCntk5yWvwcPwcyqNncV09Zj+/WVHium7zFgfP4rp6JPKevGEYjePbyJbPVOsVeOfcVA+QqwVwchtFsMQzeCzC7NxUD3ARP9MU+dfUucB6DSh5Fjh8psqLM/YMl3HmSw5uAAdPAQBO1Dmf2alBVHZ4s80rcAHoxcUaz+IxitVN4OAaAOAIa7YBxGWGBzt7HH0Z26GUnCRtB+iBWpMv5MZmilugln0IdVjNoQWYDqtQ3nq9xgpXSvZx1C+aJUBJZ3+lbrZP8hKlbgs6qbxb4EEIRG0VtkKgrysBAlqQJmsRvQmApdbcna0B+M/a0CwQwC9wuzJXoHrW0rxEzgVRHat38/+yjFzXAEKGiTJW70D1DEvdC9R9T1ZGGHLMDgDRy2vPO80bWcZentkpS42DsnbFWl4DlidYYc1nCNICK1XfJ3NwDXq5wkqt+YxGmuMYF8hVDMQH0PEKR0hE5FvH6gJpdAwohSJe4UglIqDkBBfI4hMgjFEEMVa44J9DaY5jdYF8cQ1YrAAARyphP2cXaYFrOEe5fBpYnlR5seYzO0mCE3UBvXwaWBwDAFbqgp03u3iMQGmog6ebfZC8YPUywoOdPQ7zIJqDQKoDaG6KV0pVeeNIbkZj0R62h7GUoUJ7eJm8UkxUtzg4jGWKpLOOu1ljhct8qaRFibzUzUyJlCzstFfQSeXdnjeTYc62Cluh2ZptcCbrSge0Rb6Ug1xfHiclMdraYyFpYx88NM8aU6IxxOxIzC6dpa3VOyA3C7T1nhRiwLvuh4CcTPCsw+y0eWXXK2UAoXUln+03m9id8azAEep7VBYrhMtjHGHNfp9dpAVWWKNEAEQHUIsqL7/Ir9abhUcAAG3yCjA7K6yRR1XeMl5V+yDAlBxhjbLOW0QrrJAIgL5qf3W8AqIlShWKFPlnSVY9D4ujFjyohM0YpRcVoFbLVQvOwM9brE8BAMHBcQOijgRA9WWGBzt7HEa2FAQ1KBHqhPYPL6lZoK7cDJA7FE87TAlgGKM5mKiIXXhlRYk0L5t9MDbRbM1t55Z4oNsJFZaFNUYCXK30sMSKy5x1L8Tt5pUGZ5JW5NsATaYA22J2pIwPbMyOGBNl8so8a+dJAdVhqhtnRbbOvXrvNE2hmlnn6tyrYrzz3hGy4O4W+YAgs5MUzUxjm3cOJopvAGFcDs33uDGAEGA7jwwDEx8hWB5joQokyQU77yESZOEhoBSC5XFV5AuYlxypNfLwEEBVPK8UH5ydJQUOkaCoQQkWQoxRUuBIJSjjGpzFRzhSAqAvqfYXi6OKiYpWNZhkPmcXZxVTsjgGokNoKBEmKqtBSdgBOyuB9Wb1RaphBxF+ZWsAACAASURBVOwcY/2yuljUg509jsolaxM8APxOaPeeEqA6ZERcnDoa7CqvnBa9u95DAYarKDUusqLptld5Q3Yx3oCHxWYnlFsc9Oc+groTKi0LE3vGGunWZhefL6PYBFFSbEbX3rz6f0HXqa09ljHDmMv4oA/OpIbHuwP0VV6hz66WoxpQspICk/33ZG0AwdW5nyXbslyp5s12s+nJZHYaq/cNJoovj+u/d4AaTAoMpK+QIA0OgCCoikYAxcUpP69aNwxMeHCMpcpwsU5YeSvGqAUlainFGOVYqTV03IKdlQQoSXMcIYGuC3wdryoGhsucrddYqrwCJQDK+AjHas1mSvJ1xcAEyyMgCFBEhyLrzS8qUFIxMEZ2xwclul5vdHDSYYz4n9tlhgc7exxnSb5RjEsVSadJ3pOF1XmZBe5pkjcyqypvddhyOqHNAH2vE8rW3GZDhyK/E9oOH8syXP2h8eqf+Z3Q/tB4HAZYhHwr3CEnQUBinmQTTIoxUQN27IDMwPQW8FvK5N6ytG7yCj3DcY8xYj8TrdU7IMjs9BgNsRmjHqN8JPS5bTE7yxBZoUUuOtx6nwk0hdZZ2Xv/8sHZRfM+kwVR/fdOlZffHDPMTh6aIr8unhMe2DHyrSKqGJhgWeXNmSDqLC1w2GFKwuUxVirB+Tpj5TXyOB1XBbNaHteyMG6DJcORSqDqQhzL44rZYX7XDFOiFoYxkpHd5WvDlFTzOkV8LDK7VKYVKIkPjoEgRB4eisjuTN7FYYcxUuvmgvmXQ3iws8dxlvYOW8EOa18+wM3baqU3mZ2S2Qm9yApo3T+8+B3LoUPxsL6nRCZvv3Mry8AAtfyQaSxxmmyDqKMlXzvfX+8yqm4053dYe3MqQgYFfTtg2ctVe8BPykFuyy5bZn7pLMkbq/duXglWbqMJICTF7DMaUsxOn1GWswzfZnaqvPT1DjWFjpYRW/bcb4ZUefmzjUNNodUyYnfcB5kdAQMIU+TnNSgxBaOui0hqXNR5y8iAh6p41kwQZWZrDANjmKicC86SCpyZ//6gBjsXzP1NLgwoqdaJxbGIfMswJQaU6JqJWme8xkJeg6hoaZioI6ywxppZP5R13viwWm8ZHWGl1kiY60Vi5HHGAGKBFdZImOu9zPBgZ4+jLzeT6jYbgwITEl33Riu9MXjLL0Tbi/02i4Os0MgYh/gwKOEfiv3ZD5NXWloE1B1WdkdtoBMqYADRX6+5d0nagewgCuvLa4XAWf9yVSF5EbDN7EgUzeYG+m5+iT3uM4hVXglZ2DYDIwFKjjbeO3LOfxt5BYwahppCEuYd66yE1pvvhwZMMgqaIUb5MA7rJhSdsW/kqL395RaLQ02hgzhkSw8rsNMO0DfFfsYDO4YxKmumpMmbn/PyJtV6YRiYGkQETHCWXJwhVLphYILlMQKloXPe7FJRMyXB0oC+ahYoZd4ZZgbzo4N2f4+wRlrw8how2oCSmjHisrMGPIc1w1fUIIptEZ3Vz1PNcOVRBfq89bSPJyK2mR2+3Cwraq30RnHLLzrOeh33Ki+/SOp3r6t/5hd1fTtgoDogDWgj502GOqF82Ud/MB+QsSLvg4c270xMlPBsjdjsUt/4QNAK17h6LWtQInXRbPcGeqAq6qq8MoP5JpZ1Xm4h2md2zHq5XcvzHqNhjArYeXtNIYn1GlfF7vfNPBecQmno+7aQyDvwPmvysppNm4YrQLUP3GLRPPuHvf1NmEXzeVLNqvRBScwEJWYWyBShJm/IBFEX64tqVmVpmJIaRGS89abNrEoNnmomCimPMTKyvahmoNTyGLEqkKe82aUi6cyqADVjlLDfDWVS7WNUgx3EQuCh/nzM51bGx/V9OLznV5nnqX5+DTjzYMfHlYfWlVb6IJKVsZmC5SBqHx2RvPWXRnq9BtiZAhGQKeqG8i7rIolT1A2uNwrYhaL597uH+EHE71iagru/v+ziYEBOsowDgYspt5m+hUCRdNZbbxRWluHcvEBVjB92XL3MpbuJwCWdR/FA0Sxwb023aDZ52YP5nXtVRPP2QJ+xiuZ0b5sB+uX2PnD2t++qWOWtnwfGd26I+ZXY3yFmRzJvH0QleclijIYYe5OXE2dp5UKmG7BTFaNRwWVg0s1ZlTpvyARRWQNK+owRD5SU646rF2qLZACKyRiVBpTU4MEwG8h4620ZmHq9i8oAIstSVl6zru7ntpIAJWYfNwwgEvY5FBjmrV5vGVWMkcT5dlnhwc6ehjlQTZcSaItxjs7SvPQPBop8zoNv1tRdr0Rn0WhrlwP7wMtbbOetiw6Z9W4etqUGSzvfrDfa3F/uy8q2Xm7eoedhGYXsjloD1mPZzu1wXj6YrHKXG2BS4nsBVMC638EG+ODhIitwIMw8AFVz4nCIIWDkLWtXxSEmivOsmUbK0cDnxsnbN3/o5pVgdroW0ZLMjvTz23fmA4TA5ABjL/E9Pk8yrLDeYmCigiffMqCkz8BwQVTj6rVsZ2AAIGKCqFYWtpmXC84KA0oODGNU/z+T4dJZj4k6kAFnKjWysNZQ4VClyDKeAUQjX+y40kkwMM3nvmjvR6qYKD+zMymUUt+tlPqwUkorpf505/dfq5T6oFLq7fX//l7nZ0dKqR9XSr1fKfVepdRXdn4WKKW+Ryn1gfrnX9/7+76p/tkHlFL/+HL+Ky83TBHaLb4WYV2MCxTNG6AkNEUSTzNe5d0sFqu8AiCqU+QvBTq3pmDpMlESh63Zw4NoAPSx8g48DxKgJB9+Htggqv73l9Fmx53LOpj1boE+dt559heovnOboFoGlCR5ubG/EgwBUK134/sm8D02eTfWa947jM+uaQr1nrPuzygx9L1o3w/896Q0SG2Z36HPjc/Yb66X/zy0bpiy4GyIWTfvMw5jlK7P61mVTVCyKKVBiQzYMaAkPNjMywUlxtUrOtzMGzDlcTCzKgctAwPwZ4xULTczTImU7K4BJXFr7Q0AOuXtQ5jX/70NiFrhWK2RMEFU87l3nt+Xm4wtGv8jlxL/FsA/AfDGgZ/9T1rrXxr4/X8AINFaf55S6rMBvEUp9Zta6/sA/lsAXwTgCwA8BeD3lVK/obV+t1LqLwL4bwD8WQA5gDcppd6otf7VGf67riySgU7+UqCz2HTyu0WHBFMyVDRLMDuWIhRgFgf5MEMAyOzvIOjLShwtqHmH15uXGkWpEdZ2viLrFRjoHWTO4gD3zriM0fD+chmjJCsQh2pjHyVAFFA9pxvFuBBTss4KPLtqHyipvElebjF95vepkdezKtJMdfucbb/PpL/HEsyOjaEFhBhl4edsqDkmwfS1+zsMqk/IeQfek3ELfrv7MyWMzEotN+VmCyazY2RWfblZzMyLbBM8SOU1jEi07IMoHigJ+sV4nZ87YxQWBjzUDJQBU0zGKOqt14BVnXDz1hfXmhkr487HBFFxeVFRI+bS1lp2l3o3tmmhtX691vqjE/+1vwnge+t//0MAXg/gv+j87Pu11oXW+h6AnwLwVZ2f/YjW+kxrnQB4FSrws1dh6zQD8h1LScZo8LCVyGs5FOl5TXEwxMAIFF8DIJW3D471Ssj5unkFmJ0hUCLB7KyHmB0Rxmi7EJKQxwE1UzIEqsWZHX7ealawz+wIyFyH3mcC7x2Td+P7JsAYDTGIjcxVmKFtPzeOPNkOSljM+hBDK7heaXA2+DxIyCWT/oxG9f/L8oLFGJmB9L7cjMsYqS2wIyNjM6BECcvjGrAU92SCTBAV9mZVGsaIC3aKTVlYKCS7W+geE2XAH4Ph0lpjWfZB1Aqh0iiYIOoy44kAOyPxHUqpdyilflIp9Tmd3/9MAB/p/PrD9e9xfrY3MdSxlOwASncs20N8iIkSmDESPxS3O4CtnE9ebgbIr1emSBqe4TKMETUGQUkUsH39k6zEIgqaYX+TV4Il6T67UnkBA0ra3K35gYDcTJhJzQqNUg+/dySes40iVISBsXfyeYzR9iybBPM7xNjLFvnzMDtDoE+GOZMFZ0PPgwQz2bcE7t5uz2q0pH23sFoeNxMDwwVRTTHfA31c2V0flEjNAsU9UCLldrfogYeWMeLJ4xaFyWtmjOrPj8EYJXmJleqBnYVhonjrvcx40sHO12itX4lKcvYGAH05W7ea6utwqD9rf6DU/6KU+qj53+npy+eDdR8GnFmVAdmHBGPkOGRkZozmAn0DxZfwYSvxublkHzIyle1ZCp5hRQmlWgAJVOuVYHa6rEOVl++2tM6GmB0pg4JNZkcit3FrlO+MD8ycCTwPg99jkdmaAcZThDFyyIglQJ/wbKNLbibdDJFUGFwOs8PfX1XPeAQ9poTraNUyMJvg4UDzGCPjvtVnouKSB6Ki3qyKlDyuBSWyICrugQcpELUw+xhvys04zE5ZahzArHfzMliOi16SlThEglxFQBhX+ZYycr7LjCca7Git/6T+f621/hcAPkcp9Wz94+cBfFbnj/+p+vc4P+v//f9Ma/3p5n/Hx8f0/5hLjvVMh4Gt487OOySzEimaXUXHTMWX9OySAGPkeh54eWeSx+VFfeFn24tYRkHFHHAYo2xzngSQM2pYzsXsZJvMjkTuIbfGKFAI1AxGIzG/WLwaZmcmBny2ZsiTx+zMNTuaNMzZECgRuFpB2v2wlvtEB9vMDgtEZSZvew8MgPriS3redtB9s8g/YDI7oQE1W0YNTBBV9JkdA6K4oGQYnHFld31mR8IAIi1KHCFBiQCIDqp89XPBASVJXlSgPDhsfi8weZkzRpcZTyzYUUpFSqnnOr/+GwBe1FrfrX/rpwF8Q/2zzwbwlwD8QudnX6uUCpVS11HN6fxk52d/Sym1UkotAfz3AH5i9v+gS47EwexwO+5V3pk02HMVSeKysKEin8+UJAODwhKMket54LrdhYFCHA7l5RUdffAg5XbXl5tVLAlXErZpIlDl5TNGZamRFuUWs8MFO0PFuFKKnXfQ7W52ZkdWHifpxibNELhmSiRcK6VnG93MDsflzTCIshbn7v2lr1flRm5WF/nRAUoEWIF3B0qYDTMa3LtVonw477JcsxijaIuBqYpmrjxusQV2qn3mMkZLPQyiOIxRXpQ4hGF2+vI4et51VlQgNzgA6iZh60pHz5vkJY5UgjxswY6U7O4y44lwY1NKfS8qc4HbAP6dUuoUwJcAeHUNSEoAdwD89c6/9h0AXqWUen/982+ozQgA4EcB/AcA3mv+rNb6XQCgtX6tUuqnALyj/tlPaK1/Zb7/uquJ9vLP7YFeERcy8WJmYLZGtEgaKmb4cj75DqtjvdLMjkjeYdYB4BdJffDQzdsv/ncNGygxjFFAdKVL8gLPHm9a5Unc6D5UfJlfSzxn23l58rghEBWHCorJGA0zOxKzH5fP7IiDvplmdkRB1CUwO5LMmfT9aaaINZ12KIUsPMRRwWN2GrcwUzRHhyihGltgqitd1MzAbBbjq5oxorrSNeCjl5fLGMU9WZjE7FIzmB9CFEQZBiZRB1gGQS8vb7bmSCXIwiMcmN8UMGowICoLj5rfM0wi10XvMuOJADta629AzdL04s85/p0zVIzN0M8KSz7z828G8M0Tl/myikFrZJGZku1D3BTm4qBkJkMFySJp6LCVN4CQYIyGrZEB/kzUEOvAz1sOysIAUzTHpLxJXuCpw81/t7veg0AORIm40g0wngCf2RliaEXyDoAopRR7L+aa2XHKZ1mM0TzyWfdMiYR8S1ju63CPk7mPbB7DFWm5b9C//wRAFh7hWF2w1tsO5teMURAgCw5xXFyw1hsZ8LEYlt1RwU4jV2vyVgW0YYy6suUpsTTWyD0GpnEnI0ReahxijUzFiOtZFbNeDhO1zgwoOcTS/GZcsSaBmREiRJKVWGGNLDhof9Pc41Mk9Lx5iadVgiK81vyeqteLjJ73suOJlbH54IX7EBco8gcGenlWra7OomzRITvYLCvPMOtdDMrCZJkSKbe77YF/PphM6pmdzbwyYNK6D8wif0t2F1eW1twZo+4am9xM6Z2d2eExRkNgXSLvUJHfMEbM5wyYoxny8md2FhLvh5nc2FzMjnRTSOK904CSuAU7eXiAA2SsvFtMCYA8WGKpeHkbRsQwRkGIXEVYMte7LHt5o6poXqiMfB5rrXGg1/WsSg0fzMxKSb9Ms2I0kk3wUOcNipScN8lrpqQzA9OslwFK1nlRGQlE7bNg9kNxQFS93mIgLxh5Lzs82NnTGLRGFjwU57osb77L5y7vsjxukbSIgg05ldR6t9kBAdepwbwCYDIrB62cAb5MxcpEEfMaZ7MhZgeQdxME5GZ2xBmjfJvxrPKG4kV+wxgJMDvDMlfpmR1J5kGYgRl8n/HfD04Lbk7eGWei7PJZ+uf25ujP4x9G/wC48fnN75XBEkukPMbIFMdxWziX4QJLZKz1RmW6lbdQJi8DnJW99dZFMwdEZYXGAVKkwbKZVTF5ozIlzxgleYlDlSIPlu1v1nlDTQc766zEIVLkYRdEVXmDkgGishKHKkERboOogAOi6vWW0XZelXtmx8cVx2XeQyB56eUgiBIuFmX2YdsaWUwWZpmB4Q5iD3XxAT5ItedlGhQMWDkDfLBuXS+xOBi6I6rKKzngLc3AuGZ2OJ387dk78/dIF+NtXtnLKeOwKpg8syMwW+Nw7+QxRtv3ZUmdbzZjFM4+fETfxpsP/iPg6HrzezpcYoGcxxgZ8BC1hXMRLNkMzFBewxhRPzetNWKdIUcEGLmwUshVzAJR67zAUmXIVWdmsl73ksEYJXmJJbIe2KnyRhxQkhdYIkMZbjNGHGbH5NXRAIjiMFFZ7sjrwY6PK46hQ1HiMsJBeZzEIe7q1HGKjrnci/KqyB86bNnF+IAECOCDEhtTwpXzzTGzU+2DLLNTlhqpa73EvEOFokReoAtKtp8JaYYWkGCMhpkdqfVu7zGXMdoGk0qpar0SzM5M7mbSjJGTWReeiRJhjCz3ZQH8/R36rgH8Jla/sVBGNXjgzOyYojtqC/IyXGLBZWD0dt4iXGCJlAkeUuTBppFLHixY4CzJKlBSdEFJWP0dHOZsnRVYIt3MK8EYZSWWKkMZdsGZDGO0RAYdboOzBrwSIk0TBEpDR9t5ObNAlx0e7OxpDGmlATmZivT9ES5La67Mqm+NLGUkMHTRIyAhs5IHJUPrFZPH9Z8xIRe9rcKWmdfs3xCbAdA/t8TBOlR5JS5lnIfZmW+2ZvtZk7nMWHYfhpgdgL/eIXdJwxj9+8bs9JtCInkH7staChnlbD9jEjOI2+vVYS1jY+SNBhiYMjQMDC1vUWrEOt1kYGBkd/S8psjfYGAAFMGiBn1UEFXUYKeTVylkSgBEqQzlAHhYKjroMyBqKC+LMWoYmG1wxmFgsqS+YHaI2WGAqMsOD3b2NGydWzlr2fbRiYJqUJjFGM11WalrgJ65Xpc1MjlvNs9gvik6hvJyi7qtZ4x5P1JelMhLvV3gM4uZlnWYidmZCUwCw8V4WpQsPXqVV5aBccnN5mB2+Ou1SxClZ5cMYyQ/syPTZAlUC8i6fwfrPpzBppDAe3Lgvqz2agWu4YplvcLyZIS13IyRN9ZJk8uEbvLS9iFt5FuboKRkrrcFJcuN3y+a2SXaeitGI0URbubNDThjgqgNUNIwRpy81f5iADywGKOagdnMW4MoBmOUG7ATD8nu6HkvOzzY2dOwyUn4lrXDg8LsYiYrEAUKkbAL2fBgvgyzY9V2Mw9ba2dRmImSGMwfuqSTy8BYJVYhb39thS13IL2902oemSAw45qHmB1hl0Lz6zlmdhYzMTtirnQWkErPu90UMq50/Fm2UJ6BGWiyyLimDcjNRO5H2mZ2RGaMBpgdxEscqAxJSstbMTAZMhUDQbtmHfEYmHU2MAODWh6nckbecpuBQcsY0c+Lar3lFohasGaMWllYp8jvMEZ0hqvAgRqWm3EYowaUDMjNQgZjVKTrOu82OOPI4y47PNjZ07BZwLLdiwaskc2v2S44vbVKMEbD1sgyMzs2ZkfaGnkuFzLuPmSFRqkHCsWYC0osw/Mxz6DAWtgyiySbs5nIzJl1ZocpvbMwO6YZQu0uukDJk8jsJFZmZx5XumUUsuVQ/aaQcaWTls9KubHZ3ztMZl24AVCtaYjZkZm16n/XVF1EZxnNvtcwD31QwmWMzGxNn4FpZHdkI5cCC2RbDIxhjOizNRUoKaMhxojPROlomzFaMPKmSf15d8FDGKOEYu1Dnl5s563XHjMYI5M3GGJ2GCDqssODnT2NIVkYUB2+XK103xoZkLGW7R9eEpcRJjNYDVd5B6yRBWZVhtbLLQ4qBmbIhYx3iLcX1/byzs7s8ECJvUiiH+LAMHCo8s7DaAB8SZ9tjzmFEmBhSmbYB7n7hoZmdvggahCcCV/i2+QVHsyXcKXbJ2aHbo1coij11uem4qoQbYrUibGuZ0r6TAmM8QGRMapmSjIU4WZewxhx3utbMzCorbIZM0YNKBnKy2G40ooxwhaI4jFGWWpkYZ28SiFnzhgZZmcIlHBc6YoajAcLWXncZYcHO3sayYA1MiDD7GxpjyFxiA/fyiwxiN3vuLeMETPv1mE7TwdQaqZE3IXMxh4yGSM7syM0s2P73JjMjhWkMjv5VS5ZVs41q8LL62Z2+DNGsqCkZeXkmZ3+5ZQmL3vQfeD9u2CCvvVA80bCle5SjVyYTQvTFJK+dNjGoppb6I1MaHreonY32yzGTSFKZ4wG3M2AhjHiupuVfRAVHrBmjMx6N6yRAZQBzz0uG2JK0DJG1H0o0oGBf9QgipPXgJIu2AkjFAhZect6vWHcvWenZow0nTG67PBgZ0/DDNB3NdiAzECvrbMo3VED+IzRoHxAaFB4qwPIZDSKUiMtBmZruEyJQ6oDzABK2AWzBTwwZ3bmmn8ZZ3ZkLYE3cgtL+uYya+Da97rv2ZGY2RmaBWKCh4HmDXdmZ4j5BWTA2VCzie2iN/D+lXClG7SIZjYtKsOP4TlXQIJF7YOdqigtiWCnkW/1mB3DGBQJjTFqZmDCzWIc0QGWKkeSZcS8AzMw4M8YJWleMTDhgOyOwRjlFlBiGCMuA7Mx8I8aRDEYI/McbTAwMNbedNe/MqvBznKAMWJeinuZ4cHOnsZQ5wuQGegdBiXceymGiwMZ2cfAetk3r28XHdxD3KrzZzJG8xW2FimUWCFuGRQWlm7JsSRzuOjZcvOcp+zMjtAeC392LmaHN2NUblkjm7+H/560NYVkwUOTV1iWW+VlNpsG3pPcZlPDwFieXb4E0/ad4DVZ+s+u6cCXzJmdogcegvqm+yJjyOP6LmRA082nMlFGHteXhSHizcBk6cAMDAAdHdSMBu1za0FJTx7HZXayAaYErSsdWW6WnlvyLlizS7p+PsNFJ2+HMeLUUZcZHuzsaQx1vgC+xt3WsZSwrB0+bLmM0TATxWGMjDWyNGNk0/lzmR2bVIcr+xi6MLD7a3oX313gz1fMPFkgCmgdoOSljfMAYKsxikB3vG+NDEgUuNszJYCMPM6Wl3055eD7TKCJNYOM2AXOpOfCjDyZ40IGDHyPmcYoNmYnXBiwQ2Rghi6nBBDUxTmVMbIN5rczRsS8WY6lyrfkZtowRmlOypsl5/X6enlD3oxR40LWy8u9DNYwbn0GpmDOGDWgZDkgj2MwRqjzxotNEGUug+XUZ5cZHuzsaQxpsAGZG9KHwQNX9mGXUZA7ErU1snQn1NZtB3jrtQ38cxmjJq+w7GOM2SEXHZZ9YOe1DeVLSbeE2QyguicKkGeNxmd2ZM0aJKSNB/G2LFcCnLlkYRxXOhtTMsfMjoQ8eYix5+S1yXKBesaI/N6xzMAwZ4wSS/OGa4xia2KZDrwmMjuGKekP5quFYXZotsDm3pq+LKxhjIiGCs1gfh9ERTyjhnzIGhkdeRxzViXoMSXGlY76vSiHZmvQgjPyeuu80WKIMaLn1Xn1HEXLAcaIAfouOzzY2dOwabuNfIsr+9jKK9ABlAZnxhpZWuNu69Tx8w4XtmKMkfhdLcPzGdxZFZeTVZVXeu6DKwmzMTsChhUjzM6TJm1cZ9vWyBJ57U0LvqGCrWkB8FhE6wzMDLJcCVc6+8wOcQbGAkrM79FByfD3DTBXIPCA+pA8WSl+A2Cb2amKfFNMTs87PAMT1kU0FUQlWYqFKrbkZs0sEDFvlgwP/BuQQmWMSgtToqIDxKpAmtL2t2gG823yOB4D0wcPZT1jRL73LnfkZTBcqPP294FrqHDZ4cHOnoa1AxgH0LoCArS8lpkdhkV0a41sk1HIdpkBHoiyySjM7/ELUFn5i43ZEZsxssnC2IyRLLPjmvsA5mN2uB13wAEo2d1mWUmfi0k1P6eEXY7K/+xc6+U0RFzNEI4rnTQoKUqNrNAOGTHzvixhQxvb9838Xez7sgYYI875trYxO3UHngp2jLtZX25m8lLlcfl6GJQEzLzF2sjNNotxIxOjMkaD98AAzfqpM0bl0KwK0HGlI86cGVCysDE7xKZFk/do47fLYMkzEqifz21DhQMeiLrk8GBnTyPJy6YQ6Aa3m586Z4Foh7hZi834QHqoufo9ibzCIMpyKJrf4+btPw8tYyQs++B2xW2zS1zGaGRmh21pbR32l7gHRjb3Oiss92XxQdTLD5TY18t5R9hACUD7bmit3bONwiwfwGuyjL0n2ZcOO5QLlLAxO4DU/vaYHVM85lQGJqsZmB6zUxfRVBDV3gMjyxjlFvmW+TWVMbLJwpoZI3LemtlZ9EHUEpEqkSa0O2YaZqcHSriyOwNK+gwMd8ZIFfVz1LfKDpkg6pLDg509jTQvEIfDh0z1c9phmxbl1pBwNy+FMTJrGVwvo6PmzCsgzxBfb2Hybu8v57A14GAQ/DLAWQNSLRp3cmFbDIMzKcbIBqLo+zsPY9RdkzwDUzZW3kN5nzS5mUtmxctrkeWG/KF02wyM+XunRl5WslwrKCE2m2zzWwBvxsgmVXJ7pAAAIABJREFU76zySjDrw8oF8vvB0rwBzD1GwvLkBuwQQYlhSizMDhVEFRZr5KABO7T1NtbIlrzk9dbgrC/fMvI4qtud+e/cYnZMXuLnhmYGps/sHGCJFBmT2enLDw1jlBPVPC3Y6bvSVTM7uXdj83GVkRbDzA5nPqEoNbSuXvz94HQsDUCyrZfKGGWWotn8Xkp8qbjyLmM6KMlmYozSXFvzcrT+5r9ziDGSAJP9vFzGqAGpkSybYVtv4x7HADtpXmIRDlsjAzyAZvteVHnpz4QrL4cxGgYPvD1OLes1fxdlf01TyPY9BmiA3VnkM74bY8wOdcbI9n4wvyd9rxVQNVoyNhM1xBgpclFnXW9dNIcFrWjOLW5hYc1oBNS82bAszMjYVE5jNFqmZFjGRmWMGheyLWanzkuc2TFys3g5DHY0Uc6Hhonq510iVBpFTrvHSBXpxvpMGLBD/V4EFmanbPL6S0V9XGFkhW467N3gFB3mcBrKy+kIN8WiY70cxsgFoijhlGeEDLmDC0SxmB3358Yu8sOBzu0MjByXMcos+8CVhNmeXwk3tjEmVbzIF2C5BvMyL4TNinKQSTV7w/nshtZr/i5KgZA7mkImb14y3pMD6zWGEJTurStvzCjyXQYFcRjQ50Ytc2wmLxtEDYDqOAqQkudcLTLtulOuSt4Afd+FzPxaEZkHGwNjwIQqmHKzxTCzQ12vASVbIMqAEuJ6W7lZT8ZWg0kqOIOFKdFMJiqw5TWghPhOD0fBjmd2fFxRFKVGUWrxDqvrUOR0hF2HIqebPwZKqK50Y/vALprFmZ2x9c6Vl8mc9Ypb7qCwbb1tYUsrZux5VZ2XyewIF+O75aXthRWURGYviHtclOLNEGdehnmHs3lj9jenM9XDoI/+PLTft21QHYUBSl2dKVPD1WSJGAxM6lyvIst1DJgZ2t8ooIM+6+fGZHZMkd9ndkzegAiitGUGppldoa43G54paX5NzAuLNXIjuyPK4wx46BsqBAZEEcGZjSkxckSqAYQtr46WCJRGQdzfsDSMkUUeR2jcXEV4sLOH4WY06J3bnUAJiTFyz5QAIDkCuQ7bRVS50uXCh/giCuiHrYMp4XQsxzq33KJjiHngdG5H10v4zFx52cDB8jzEAa8QN7mHO/nVntMLu+Eiv2UIZEFUFNCZh53yEg5cV1Oo2QfCs9bKXIeLcQDIGMzOMFNCB5MuuVkc0AG7sxnCASXO9wMDRDlA6ix56+KxKSYnRmmRsZm8VBmbDeyYXwfkWRULA9MwOzRQYpimPgOjmIxRAxZ7RT5inozNmreRx1FBiS1vbRme0PY31HYQtVQZUn+pqI+rit0ORTpT4nJ5Yx22jk4oCZQ4ZR/0YtGZNxA4FAfzcuQkFVAc7lgGpL0F2hkj2/5SOz4244Mqb0Cm45u8PTAZBgqBEgA7fRAV8UAUYC/yua6KY7IwFmPkYB7Iz0TuXi/le2xjEKu8dNA3VjRXeYVBCQOwj4EHcl6HLDcKGMx687kNNYUYzZtRECUMzgyzw2VgLAP0ARFEGYYlWG66hRmGgwqibAxM2MwC0fKaGSIVb663kcuR85oi38YYCTM7MU/GFpXDMztgyuNiK7NjjBqIcr5LDg929jASw5Q45Q7CsoRgnsM2mqmz2HTdhbXzcahqzT59f20zGtROqMsAIuYwUQ5QsggFGC7r/vJmrfoGBUAFojjrDQOFsGfjbJ5djluNDTywmZ2ZZGz2vHRZWFlq5KW2zn4APJmrc73ieQXek8LPgzsvfxZoWNZY/R5JHpfb35NxyGjeOGWCdBBlzRtWxWNEBCVGnrV9vwyP2Wnua7Hkpc4YGQamb+Vsfq2I67W5hYUzMTtBxGOiIgt4aH5NzNsyO8Ngh5rXDqJ4srvLDg929jB2G2SlH7aDDIHAYTucd67DlrHews6UcIpFl8tbFCi6jM3VwebkdcjuopCe18X0VXI+JuizWYbPxGZwZGx2BobPlLiKcQ4r5wIlLNmoK6/4YD4jr3O99OZNUzQLM33m+RzOy1+vWx4n/Z6ciVlngCir0iJigp0Ra2Rq3ua+loHLNAH6jJFq7oHpMTtMxigwBgSWS1BBNCgILe5mAfMeIxsoCZiudM3n3XelMzI2KrODFCUCIIh6f2HN7BDd7i47PNjZw3AW+eaQEZaFSRy2Nm139WdkD1sOE2WGi53yOGFNPuewdRk1RCxLVff+cpmdoeKLs940LxCoFkBv55VlM7jyOJN7EDwEPBmbLW/EKEK11sgK7WyGsORQl5mXwSi7GIKIMce1C7NOeu84lADmu8I6L1yNAGFmfREpZAWPWbdaWku7bDaghFgsGqbEwsBQZ4EaZmcLRPHy2hiYhjEqaHkDC1NiwBp1xsg2A8N1jwstTEnD0BHBWazrvOHwLBCF2SlLjYXOkKkF0Lv+wDM7Pq48dumEUl7cTvAQ0iU7LoOCiNEddzIanNmlZvbDDvp4s0vDN4MXpUYpDFJZWnTXcxbRO6xjsxTSDmQmL4fNGCps27zyBgUcZhKonmE3GyU7o8GSzzrkhyymxAGq57K8N38XaxZI+HnYaWZH2NCmAamEvM7mTQP6ZGeiolCRgBngmtmpwYOm3atiG/jngijDsPQtorkubzYL47nyGsMCKmNkAyVcC+5ID4Mz7iWzsc6QIwTCTQamAVEEZictyspxLVhs/zDiMVGXHR7s7GHMzTxIuxeN3cdQ5ZU9FCWKL9f+is8uCbg4zeVeNKidDxR7oHc4L2/GaKjAr/IqhjxumCUBePsL2C/p5DCTFQMjb1CwUyefldd+b80TOYPoYNYpjNwuM4jSFv0x57zYwSqb5nZnt4jmyA/HrL05duyDeetisenIT4xgBOyExLyGgenf38OV3dldyIw8jpa3lZv1wIOZrSGCnciy3mbGiHi5ajPwHw4zURTGSGuNWKfI1TYoaUEUEeyoFJlabv2Mk/cqwoOdPQyXLIHFPDgZDYGB3rmKr5lml6Qtdsfci+h5XTNGij3QawNRHKZkEQVQfdocVQeb02G1MTuVQcEceemDzU1u5/di+mfXXnp5eTMwHLOGXeSo8s2beeSoIrNA4q504+AhJdwLNGY00v0z4nnFXemUPLMexiihyGDHFMVbl15GVcEbU0GJZQaG6x5ncyFr88paI7eyOy4DMzwLFDDkZoMMTESXsWWFtjIwHPe4NK+YnWIobxQDAAqqFfklhwc7exjuGQ2BjqV0Mb6TG5v0jNFMeRl2wy5GYxFx5Doupk9AHmeZpSBbROflIKAG6gFkQuEFAGmh7cwO07LWnpfeEQbGDQrmshomFYuOznh7ObD0941eNI9dOgzQLv907cN8zO9M73UBZsdthEFv3lyFZTiLWe/nVQq5irFgMzDDMxoRlTFqmJJegdswOzTZXcOUhL28YVU0B8S8jdxsK2/1a1XmpLw2uVkU138PYb0uBiY0eYvp6zUMzFBeA0pQTF9vC3YGmJ16X3RO29/LDg929jCu4n4ZlhZ9ZPYDmGOwmZPXdW/N3IYKwh3hgCePW4QWBiYMWJd/Ds1RVHkZ9/eMzexQ519ceRn3I+VFidLCwLBm5GZ6Pxhg4DI+oKy3ZX4HLMMZMxq7uEvOIRut8so2FxYssLOLEkAWPMwNzsTlyYxmnssFslAxQhCLRZslcO2aFWha3gZ09PPWoESR81rWW4MSKtixWiOb9RKKfKBixgoEQLApn1UGVBHy5qXGUmXIg3jrZ2FcgwcWKBkAUTVopeTNCpN3e70GRGmqxfklhwc7exhzuabNrUUfdo/jM0YuUEIpnF2HF6tjaTrNjtkEllxH2B7ZNjxf5WUwJa7ZmjAgdfGBqqhzgR2OtfcQGwdUYJ06C+RsAgSMonnEAAJgunpdgfW0tOEKazD/CvKyivwRy3tq3nZ/h2f6un9mWl63hTw9rwNMMpj1JC8RBQpBsL0PuYoQIyfdN9QwMGGvEFUKOSJEVFBiy1sXvFRDhQbM2Jgd4nqb9diYHWpe5INMCQecpXlZfd4DeaOIDs6yos47KGMzYGc605fmJWKVQ/f3Fh0Q5ZkdH1cVrhumOR3WRp7hnIGRvTch5si3HHkbWQ1Di+4a6J1Li06R1SR5VYwPHbac2SWTdyiiMIDW9EsDnbM1VMbIAc441tOZKy+D2XE9Z0F9iSnHJcs9S8FxE3TNflBkmDvM9IlbI5tmiLy8s8pLf5+5B/Pp4MF1+SfvstKB5g3D7W7MNa3KS3ifzcis294PpYoQoaA1hmzgAUChIoTEIl9ZBuibIp8MSup/ry+PYzI7oc6qe2B6MzANw0WYXcqLEgudIVfbjEZz3wwR7CyQoRjIGzDkcVXeHOUAAxNFdCYqqderB5kdA6I8s+PjisI1+8FxTXNbAs/j4sQ6ZHayKGXIaoRd6ZyHLUdW42BKOLNLriKfJX9x5uXdh+O2iObI47YLujYvz5XONWfEYkqkmZ0dQBRv9sPBeM5klc1iqp3M+kwD/9JubAxXOvc9XPPIMLnMelg3EWx5qTJB2/usUDFi5KS8gcWFDKgYoxAZ6b4hK1NSy7kiIrNjna1hyu4inSFX0fYPGDM7aVExGkOgpAFnZAamQNm/oBOtPI5y31BiGBiHPI4Cdtr1Ds0CGRBFvM/pksODnT2M2WVsM2nRpa1PXYctx71op30g7u9ch61tBoarnXcxGqy8DoOCtChJh3g2YlBAl8fNJLtzdPKBqhCVNxrhy4uGZ2CMfIsjRx1q3sw08M+a0TByVNkifxcrfflZTIFLW13MurARBotBdMlRmW53tu9wqSLEKEh5W1nYdoFr8lKY9dCWVylkiBHoYnJOoAOi+gV5XeRT5HFlqRHpfJiBYRgfZLmuZWF2sEORxyW5XW7WzBhR1ltUDMwQKGnBDk3GtkA2KGOLmhkjL2PzcUWx2+WfdPmA+ADyLhp3catW/mE7B8PlKpoB+mFrBQ9M0GfPyyu+XHIzYAZ5XEBjYMpSV0OnLnmc9GWEndzSRb5SChHxjqSdZJjCjBHHSt/1PV5EfFAibrgysxvb8GwjfX8zF0jlgMm8hFLtmdMNrvxw7P1LbRLa8pZBRGd2GlCyzewYxoiyDw3oGGCMChWSmZ1IZ8gRAUFvLxgzO5UL2bAsjJM3KQosLbIwI5ejgBKz3iEGpgGBDBmbHgC+ZrYGFIYrL7BU+SCgNrI7ChN1FeHBzh7Gbh1AYWbnCT7E5V3pCgSqXdtGXmZHeKyzKM3AsJi+wi7fWjDWmzlldzxZo/Tln22X2fa50a2nXZ1xTm7X7Eebl7IX4wYFHFByFVb6JLnZLsYS0pdeNq6KwjOTLMOKHcAv9b1jcYHkXgZre59x712yvXdKFVczOyQGxmIkgHYWiPJeD52zQDFCXUxm1rU2DMyA3CwIUSIgzRil9WB+OThbYwwVKEW+I28zYzQ9rzESGGJgWnkcdR8KaAdjRJHd5fXFqXoAUIeN8QEN/F52eLCzh+EyKGi13dIWxvzZGpfLGwecuW/wpuW1F4pcUGI5bJn762I0APo+uIwEALqscYzZmcoQaK13cI/Tkw/xcUDCu7/HnZt2EeoujBFHDiXN/L7srPRd751o3vXOZ1jByCvtAumyemdeVeBqhgA0GabtriwAKIMYscpJn1vDWAwxMDVjRHnOQp0PWi4DlfFBrKYzRgaUDLmQAfWMERGULEbkZiFJFqYrI4Eh8BAYxkiWgWHJ2BxyswYMU4wa0uqCUzUkY6sBEGW9VxEe7Oxh7MKUyDM7/EN8aK7E/B5noHeo6845xLNCOwtQ82emhvuw5bnoWeUZzM9t6O4TgMcgJk5QQmPOXEC9m3eqPG4XQMIGO645I4oc1TFTYv4+zqD7UGGnlCIDP+dlpUwjjG6ObvCskccZI/F7wxhW+rsZVtBlYbZ7uAAeszMUMcMqO3W4S3JkmK57uHRQz+wQ8jaXXg4UoqWKERNc3ioGJh2WhaEGO4S8aV5ioXIUA4P5AFAQrbJby2VZGVtluVwMy82aWSA6Y+RkYAggKskyRKocZPkMOKMYNWSpuQjW4R7nmR0fVxW7uSLNc+ma9P0GnI6wcQtzyR2o+zDWAZRmSriacet6A0beEaYEmP48aK130s5Pzesangfo95S47HWBehZoJhlbNCuzQ2sCAHZwFgU02Z3brZEh39qB+Z3t8s+5LiuV3l+m3MxuTc+ThY03Q6TfZwzmzJFXB9VsDcXqvR343wYQmjgLlBXVYP6g3AzG+CCf/JyZvDZmx1hlT2bWc4csTCnkCEnGB01eB1NCyluDs2EGhu4el2cWu/BOXopRQ1HnVQPsYbNeD3Z8XFU4D0VOcessDnjWyMAMncWitHavOZ3FxNFZ5LpDzTHwnzhkd7MZFBBld03BLAyixgp8ahE6mjeq7KEp7nHjzA7NVGEnedwseWlmDa68YaCg1IyXdDLkW8MzRvT7snZ5T1JBie3SS+6M3Oj3jejONypzlXaXFGC4hkIH1cwOjdnJkakYGGjmlUGMSBWE92/NwFhASRnQrLKNq9fgwD9aeRyl2bSwWC6bvCHBPS4t7PfLcO4bSjMz8G+Xx1FAVJGtN9a2EY2hwvT1FlnF7Kj+3UhAA7IV0bDissODnT0M19B0xJBDNZaqM1if2i695FiJuuQDMbGTb9YyR95d1ku9jHBcdic8A0O0rB2zW46IxdcubAYwvbhtC1BLB1tils3xTFAtdqt/3/5M0GY07AYFJi915sGWVymFOKDdkeS2vOc1WYAZBv53cbubqciXvi9rvnvD6N8312yN+X5TmoRZUVqt/3UQY0EAD0WpEdsuvUQFShbIJ5/zzhkYtMYHk2XEuf3SS7NekjyucOctVIQI9NmaYVBSFfkUUJLV4ME1W0ORx+WpA5Q0zM70mZ3CzOx4ZsfHkxjNIT5oUEA/DBJHZ5F1c7WT0XgC5Q6OS+K4HVaXq1f1Z+YCZ9PWW7EVwxc9AnTDimyswCfKGkfBDlHWuCuIknazAioJl3TRDBgGRrbIr/IGPPmWY3aJ2lwA5rhU1C6Z5IKSsXu4pK3eWbOCO7x/KcyZC5Sw7w0bYaqlGXCEESKCLKySWRXW2RrDGE19fht3MwcooYCzxqBgSG6GVh5HA1GWwXxU7nER4b6hNC9qIwGbPI42Y1Q0MzCy8rgWlNgZI8q9QDo3MjbZGaOrCA929jB26SxSDwOb3IHrxjYqd5jpvhY6KJE/FF0MDMvNyjXQSx3436EArfLSmJKxmajph3jFOliH8o0RBpXZmUmuA4xZRMvKoQAzWzPDzA7RoMB1D1eVl7pe+/4aeRz1+wbMY8E9xz0wLvDAabK48zLvwxkFZ0+Gy9vYPVw6WCBUGlk2rWCs5FuW+2XQzuxQGRhX3koeRzMosMrNqODMNfAPY6gwHZzlWYZA6WFQgnbGaGoU+TgDQ7pcdQdQElJkbPV6w0Fmh258cBXhwc4exi4a9yflMAB2lDsIgxJO0ZHldje2BefGcYf1NHV/i1KjKO3rpXZCW2mRbQB5LrkZFZyNGBQEtOJrDPRx5Dou+RbAuA9nF2ZHmCkBqj2StlwGjAX3PHlpTZbCeukl533muoeLNXu3UzNElilhX9I5h5FL4TJy4cpyhz83M09R5hPBzghTooMFWRZmHfgHUAYLkkGBczAfHWZn4nvSGB+48lYzURMVBpmDgUFtlY3phgpOuVkQooRCQJgxypvZGjsooTAwBkQF8UBeY8Htrad9XFW45A4Ar0iahXlw5mUyJTMYFLiKA/Z9OFZL1XnAA/Uywl2ZnalFfrIrYzTVSGAH4ABw5HEWkMqUFwF2Nopq5Ty+xzTr6V2AH5V5cOWNiXlH7cgDolW2y3KZK4cae86EL72MGBbc7ry8fRh1wyTMIGaFFm+Ojb13TDHdFNcT8lazKsOuaQhormmGMbIxMCC7vNX3y1jyajOzM5EBT/MMC1VYQUmpokp2N5GxN7IwDIES0OVxzcC/DUQhIjI7Vd7AMVtDMVTQTV5Z97irCA929jBcxThQH+JETf4oeCDKEuyDoUwN9hg4m+nSy6mH7ZjcgSwL26GwBaZr58e6+NS8u3Tbqz8nfR8ObQDZdZdT9fv053dsjyMiozHGGFGtp3eZi5K+hwsw7nH09Vo/u4i6v9r6fQvqJpS4hXxzb5i0LIwju7ODh0XEk+WKW8jv8J0A5GcFTXe8zKYNjzstlwEgXCAgyOPMvTXl0H0tYBgJ5KV9BgY1s6Pyyc2xLDWWy/b1Rigm582N3MyWtwZRU+sHp5UzKhBFkcfpOu8gKGG4vJnncpDZYcwYXUV4sLOH4Tq8AHOI0w5b2yETBgoB0QLWxcBQDy9gZBaII7tzFB1UcLbLnSpV3pkOcWGmhGrVOuYURl7vTPK4cakZT4bpyr2oLaIpd1O48pJlYfVclLU7HjFld65ZIGqRb2FgAPrs0tj7NwpoYNLt1shgdnaYraHtg93IJWquQJj2uTUukGPNMer7QbjpNnYPl+mOF/k0sJMVJZbKbuWsAyOPm8gY5SWWjhkYBDEiQpFfWS5nVgbGGCpMnq1xuYWBfi9Q0cjNDoZ/ripjialNi7K2iB5kYExewn1DZV7nHZSbBSgQ0ECUmdmJB/aB4R53FeHBzh7G+GFLH+i1FflA3Wl+wi7plJZnjFkuN6Bk4j4kowwBTRY2JtWhGkCMHeILYpG0q9xset7aFllY1jhmqCBhPT3bRaiOPSbJzfKRO5ICmmvauNyM6PLm+B5Xfx+NAU9G89KNJawzO0y5mXQTAHDvr2HppoK+1gVy5DshzPxSm01N88Yy26iiCpRMBTtjg/lmJoRifBDbLJcB6DDGQhXIsmlzJWlz6aUjL8FQoRjJWwYLRKqY/Jw575cBUAZRLbubCHZcRgKowY6aPmNk8oZDYAdATmSMUFR5o1jWUOEqwoOdPQwXUwLQrVpdzA5gDvEnp7OYOOUOtEN87B4Yqtxsl2F0YM5DnCjfshkUzDVjNFuBT8s7LufjDaS7c9MaAaPggWk97WpcUItx2z1cQPUMzsLAhAH5UlF3Xtp6XW6NSin6e30XpkTaoIAIzna5IwqY/p0w+yZ9Do3N3ZmCcaqMLcndA/+NPG4is5OMGAmYvPnEu1UK1/0yMDM705mSwjWYj4rholhlOwfzAZQqrg0VpoISw5TYmSgK6ENtcGHLWxCtso31dLgYYHYY9w1dRXiws4dRDVpa3F9ALzqycq5D3O4WplSlcacc4vlOh/hEN5URe12qxn38EKfJt3aWhVHlWw7JUpWXKvtwz8BMB6m7fm6yMhWu1XuVe6QAmwiAxz67KAxQlBrlDM50tBkN++wHwJgF2qEpRL3E17VelkHMyD7IMzs0A5Oi1Ci1e86KknfccMXI44igZLSxINsMCeriXxfTmR3XwL+RGE2Wx2UZIlU6GRigdRXbNXLXPTAAENAuKy0yd96ylt1NPY+N3MxmJGCsvadeVaBHwA7VgnsMRBVBJbubKo9TRZU3Gspb3zcUEtzjriI82NnDyIqy6dgPRUS0ls1y3Rx+g3mJRcfYeimuU+awta2XagFr/rxtvVQ5SSMBknYD2sEOGCAUB2OsA7NzO6b1n76/I6Av4IJJtwU3hSlp7q0Z2wuq492oQ9/0vKHlHi6gZn5JMlf77EeTdwZmh249PQai6HJf93qJ73UXA26eBaLRiI3RaOVmwqAkojVvGpnrqDGKbLNJRTUomejGZowEbEyJKf6nMkbGwtjmQtbMGE3Ma0CXFTyEC8SqQJZPK5zLzC0L00ZuRmR2wiH5FmoQRZCbGaZk0EgAgG4uV50Iohq5mZ0xqmaiJq63MIyRw4LbMzs+riryUg/e8WCCqnHPy7LpnA3Fgsjs5IVuCqyhiAkgapTRIBe3Ru5gd3Dq/v075y0NiBq7t2a6xh0YvvOj+/dN3V/z5yPL80DXuLvzUuUkrXbeAvoi2nqbfbAyiHyDgnE7XOFnmMp6liPMA4P5dTI7RPmW694agCm7GwEltPW694Eya1WWleWyDTwYeZy0IchcTAm1GTJ6D9dMxigtszNNYlQN/NtnaxTR+KCxXLbmrcHZRCaqYUoscrNGHjcR9DktlwEgXJBkbI3l8tBgPowrHUEeVzhkYWiZnanvBzWWV9HWq0ozE2U3VAjhDQp8XFGMu7HRNe62GQ3A3GROc9dxHuIEF6exjrs5xKdatY7KzYj3R4zn5c0CSWvczZ8fNVSYDM7G8hJBVF1U2UA1uUjaQRJW5aXJMIHxPZ4OVKu8tnu42nm26c+ai/mNwwBaY/LdFOMziDR3yXwnEEVrCtlkmFVe+myj9D5kI983k3eqa9quQH0qOBt7r5MNV0YYZW5eO7NTMzATZ2uy3H3ppaovK9VTwc7IPTANs5MS81oZI8Nw0fLamAddy7cmy9gMs2NljGISiELukIWhYnZoed3rLYl5DYiyPWfU+4auIjzY2cPIy9LJ7FDlZhVjJKtxN18StzxuetEx1nGv8k6/n6PNO+aaRmMIxosDKoiyrJescd9tvZMtSnd0IJv+ubmZHTbDNTpjRGM0ujlsuSnMjsty2RTq0wvRceah+nPT87pBFH0w37lexr1AY/tAA2fuvBSGa+y9A9CsstumxXBec1UBxY3NlZcqT85HQAk9r1uKGtQyNk0c+LfJzQyDUk4FO41b2HAx3oCoYho4Kxr5lp2B6f65XcPItwKLRTTCBULCfUON3MzC7OiAJguDkYUtLHKzIK7uG5r6fijcn1tJXK8yz6UDRFEYo6sID3b2MMaKDqr16W6DtzTZkrTGfWwIu8o7XeM+1skPqRr30dkPGrMzVsyQi/wR2R21sG1kdyPGElTtvK0JQJ1d2tVYguPGNiZBpJhhOJsLRBOIMVkYdZ5trMiPw0qWO3X4Nh8DURHt8s+81DvMINIG/seYM+kZRKAq1Cc/C00zRFYJMNYMoTYXxt6/XGt6W15T/E9lYMoyFYERAAAgAElEQVQRy+UgNG5s04r8Mh0DUdXv5xPz6pHZGtQgaursks6M3MzO7ADT96GVhdnyLkizNc6Bf7T3DU2td1q5mQ2UcGVsdqtsiivdVYQHO3sYY6CEIs/QWo+6vFHyjs2qALTiIBuZVanyTte4N0W+heGiatzHZmCoTMlYMUPvts/VCZ1n4H+MJSG70o3IgDj37GRFCaXscjM6oHQzv1THu7wYYX6Jc1FZMcJUM+Rxc7i85YV2ytg4oMQN+gjv35H5LaDah8lNgB3e6xRjibEZRKqF/Oj7lyhPHstLZXZKAwossxSNPG7ibI1hbKzMTj1bMxWcoTCgxMYYGTnfxGH30s0YmbzZRBBlmJLQykRV9w2lEw0VjEW03eWtksdNbeaNgh1jqDDx+Q3KbCRvzRgRLPovOzzY2cPIR0HJdBlFMSIfMD+jHgZucEY4FEdkS1VegjzDrNcxu0TRuI+tl65xHxtGp7IDI3mJxUE68jxw7zESn12a6X4OkzsO7HIzsllDbrd67+alsFzOvOS5szF5nJE2yjJRFFBiZhBdoI8iyx2To1Z56Qy4e2ZyevNml7yk5tjYDIxSs+yved9LM79NMT114D83A//D1tPN7MZUWZiRb9lmgWKaPG7MhayVsU0EJSP34Rir7KmudA2zY8lrDBWKqYyRASW2e4FCY1Aw7TkLRuRm5r6hqc9vOAZ2VD27RDC8uuzwYGcPY8zKmXIzeOOS5SoOCPc87HQoEjTjTd6RDjZ54HSkmCHLKCzrDQMFRdC471zkS+clFgdmHfbOLW12addZK7I8TvheIKBiJ8eKW4BQ5JfuYpx+MeNujBGlYHSCEuLc2W4ziHqSPG6XGUSKLGyXGcQ4mn4J6tj3Aqjf6xONXHZpYlGaYzuBPhbD5XZ5k15vGFOZnZohsBS3QcPsTMybu+VmRh43ldnRY7NAEU121zAwFhkb1ZVuDOw09w1NBFFjTIm5v2fqOz3chdlBPvmdHurxvDHBAOIqIrrqBfiQDa018h2KJKrczFnkMzp1riJpEQV4vJ5mb5jtUhyQGKMrLGYI4KyV3cneNzRqPT2TK11EZQdK96wVXSZoXN5GZIKUyynzkfunjCyMUOC6XRVpl5XmpcbRYvhy1SovUQo0MgPDuRh3F0OFsffpZs7xGcQomD4LNPb8AlWjZHITa4f3upmJmpR3h/d6PJPxTExguMZnEKnuku71BkRmRxfm0svhYryRx02Vm+VuGVsDosigxJK3kbFRQcmwkYAiXq5q5HFjVtlT3ePG5GY6oN03pMp8JG+MmHBp6xg4e+7pYyB8DPWUxSDiCQrP7OxZjHWogOqFPlXj3lzKOCLfIsuhXHIzlixhvHMrnZekcd+hY0kpDsb21xQ505m+uviyPA8LMjswkwNZ7gapzWwN9dLAsRvoCZrmvBxjNGjOf1nhvi+rfSam7rHbctn8jMIYjRmYmD+3a+w6gzg17y6zKhHJcGW3GcRZmPUZmJIq73S5726yu0D+8s9GHifM7BgGZeI9OzrfjdlRJc2FzAZKGtlcSQMldjc2GsOF+r/PZrls8k6V8zWyMKsFN2294YiVMxpDhWnrHWNgjIxtMmOk6+fS+pwtEZSZVWr9JIUHO3sWYwP0QPfOgN0PhHYwdAw8EGdrRg5x+kC6MMO1o4xickG3y0wUxRVpRHYXBAphoAhF/giz08jChK2ym2eXynCNzNZMtgzfTXZHcatJd7Rylr8PhwbQslEGxjjpybrHUSSIu8wgNu/JCevdhXlYhEHlrjbhmWiesxFQIm2dbv5O6l1OY+/JQvhSZ6CS/E43q9jFPY6zXsvMTkwFJeYyzeGOeuNONpExMtbIyiILa2Z5poKzHZmdqaBEjVhEN8YHE0FJy8AMz0ShcbubNmMU6BEGxsjupoIoPSaPWyBCPvn5jUZAFMKoeWae9PBgZ8/CHMy2jjtA61iO3QQNtMzOFI372KVrTV7hTl31s+nFwS4dywWrOHAzRlTZx5gl8GTZx8isCvk+nJEiNGawA8C4jG26vHO39ZIsjEeKfKoEcWzgn+p4l4/M1tDvSHLP1pj1TmKqdyjyjQvelPXuYnnf5p2yXpN3rCkk+70AqkbU9LmwHZo3DLmZOy/lfbYDE0VZ78h73czAYCLYaQfz3czO5ELUuKZZZ2sWG39u1wjGBv7rvFOZkqCRm40wMFNBSSPfct8LNNnSujEocFtwTzVUaNbrMCio7huaBlLDEXCGcOHBjo+riWwHZoeinR+z/NzIO6ljOd4JjUI1WVKz0+Atw+VtbL1UGdu4BTc1rxucUfNawUNABA9jMzuEbnuVd0TGRnV5y0sEDnto8z0siBbGu86UTMs7MgtEdjcbWS/BUMHMILqaNzEFPOw4qwJMBVHjsyrmWaOBMzeIojIaY6Bvct58/L0TBpRm0/j+hhRmvRg/N+dYr3ERCyYWjHpEFhaGhjGaVtwaJsh2D0zYgBJZZidsLLinzsC4GQ3FBFHWvESjhmjUoKBe70RwFo0xO0SGK9IZckSATaYWLqYD9SsKD3b2LHZhNEgytl2YkmZ4fAqI2mFWhTILtEMHkHYvkBlAHrGAneFy1Yg0Y7SD7IPCcI0N3jY3pBOLA+tsDa0QH70hnehstuvw/FR5HDA+WxMSvm9mLa7nN2zMJWTd2CjrHZNLVnmnz4ftMoMYEmaiWmt69wxilXfK+3cX5kFNZtZ3bd5Q7nICdpGFyTPrMen9u8s5NMN666JZ6YmgJHdfphkQ5XEG7FjvwyHOAhnwYJ0FIsrjxgb+DSiZyj6MMSXtzM5EBmYElCjiZbBjMrZ2Fmg6iMqVw8csiKpnZuKFzlcRHuzsWewqWwKmHeJjN0ED3cJuijxut0O8mGgB2xQzo7IPYsdyZHaJcsFhlddtAUvNOybPmJp3N00+Ja/b3aztistq/Snd9iqve3jefJ5T1wvUFtE7fI8pJhtj98BQ8441F6q8U2YFdxn4pzRZxkFU89lNAmfjM4ghge3b7f1b5Z3ysaUNQ+tu3kz9Xozdl1Xlnef+Ht7MzgizM1mWO/K51a5ek0FJ6QYlIVHGphpmZ7hobvPKDvwb4wNdTpXHjYEHw+xMHPgfYWCoYGeM2WlngaYaFOQoEADBsBumJrjHaa0R6Ry5sqwVaP87yomXq15BeLCzZ7GLHCoMpx/iu17+OTnvju5FwLRCdJdihnIo7rJe0qG4o0yFzmiMzezM4LZEWG9T1Fk67uS7ZYqyvqtIljEaG56nrheopHo7fd8oMzA7gIcp342y1Cj1OKiemreZQdwJUBJmYEYuHQamzuyMf48pMsExI4zuzyaByR2YM9ZMn3O9HIv+sfev7AwiQJvZGWd2qmIxmDyzY1zILGDHyOMm5lWFyWsxPqhBiZrsmuYe+G/c4yYyGoEey2vc2KbmdYMSs14tzcAQ5WaxzpAri5kCuozR7mCnKDUWKkfhYnYa5myiEcYVhAc7exa70vwAbfDWKXdgDN6OFc1z5Z162JqOpbv4mn6Itx3WMc24vMsbpTjYSTvPAX029zi1+ed2jbGLKVuJ1fT1OveWeAcMMG493TC0hGdtF/BAmoERZqJ2sXKmDfyPzyCGBKC6K/NQ5Z3uhumSx1Fkgg3zMJKX8r0A5nvvjDP28u9JitvdaPOxLhYngx0jC7PJrGrZktJUxsjG7FQgarqMzV3kN/8dwnnbmR0qs2NzYzOgZDoD48rbyO4IcjMXKNENw7X7/maFRowchQNEebDj48piymFLGZDdqWAkdEJ3yyu/XqpV9qgFLFG+NWaoQJbdibvSjRdJlBvSx2QfSinS/Uh5qd0X4jIYI5dkicI6tLlHgBQx95jLG2VmZ9cB+iqvMKNMAKo7fY9nmkGk5G0YzxH5bLWGKXmNbNT93ik1Jlll7+KyOdfMThioyYYg88njRvISZ3ZM0RpaQIkpxqcyMGOXUzZMyUTjg7G89FmgkfU2Mqtp6x2ziA6IRX6o8/GBf0yb2dFaI0ThBiW1jK3MdgdRWVkiRoEycM3s0J6HqwgPdvYsdroHhtARnsTACOeNCbK73S7Lm36I7zITxZHHSR+2TUd4hIGRnq2h5k1zjcghN2vzTgOpaV66h8bJMzsjBgWEBkCbW941rdhFbsaQWe0y0zfls9v1e1zlnV6MO2dVGK5p0uvdVb41Pe9urmkAUJCMD9znENkNc0R2J31fVpuXyHDZ1lsXi+FEsGNAjG22hgqiGrBhKXDJ8riGKbHkpc4YaTcD0xg1UBmYwMLAGPngREOFEG4GRhEuK81Lw8A48hIYrrxmdkons0O8z+kK4okAO0qp71ZKfVgppZVSf7rz+7eUUr+ilHqfUuqPlFL/YednR0qpH1dKvV8p9V6l1Fd2fhYopb5HKfWB+udf3/v7vqn+2QeUUv8/e+8ebNuW1oX9vjnXPvfe7gttAHlI09iAGI2RGKXLGApjklK0oEhRKcAqWxuNwYgJKQIaK1RCgamQokKJLzSlqHRQQEIMYgRFRWiB0MUrEpB3g10VecZQ3dB3rTnHyB9zjjn3vfes8f1+3xrjnL33WaPq1r337LW/M/bca805vu/3+oIn81M+mUUJb0MaGMZS9YKHOOM6JXHRuYctoD3EqYll4CFOCZADCd7cfgf5Ib7TPrzDgd701X5nS91IPkeuU5Ys6GzmZctsKEmAxjZnTujeunkIHJrZ9xkQo4VxWpVI89DWNY3RTEYaYNZoBFARuT5IlJeXBSzvX3m4UNwwnRy5KGJU1XCFDGKcJjWq2Vlfb2c0O+XwP4abEgfZibqbnasbdI8bU50WNgRpbEOekGBnBf9D0KBgzHPV3WwzahBobNOcccBc19ZsbmwKjS3hgAnpTMMHAPiQNwG/+S3AGY3XXVp3otkB8DUAPgbAT77iz78QwHfknH8NgE8D8BVm22/0swG8lHP+CAC/C8BfMLN/bf3a7wPw6wF8JIA3AfjjZvavA4CZfSyA3wvgN66v+d1m9ru6/WRPeO2Wqm2nzdzDIK6tqT68QockX1vTi1YTeYizBgW6GJ3TPERzNM7ly+x1dbel2rUtdSOhrbW6m1V2IASV0uyI1zfnjKPnbhZAaJm8rIh+SRpaBKyn22tgGCQ1PryhLMNDWqDGmigKidKvLzO8KXRUyWWTQnaGkG368r31JipuaX2OxrYcZ6LIzjnkofy52kQNpFZlFLVAHlKyGRSIdCi3bpDGdsgnzBW6mW1NH18357w0DwyyI+z3lBIeuXXXZkfY72lOuLG5Whe/4ZOAT/gS4DXvQ9d9WutONDs552/JOb/zMV/6ZAB/fn3N2wH8NJamCAA+5dbXfgLAtwD4xFtf+4s55znn/AsAvhrAp9762l/LOb875/wSgC/D0vw8iLVN3JnDTGjC2vbQweVoXLDfbkYNfR7iza2yGcOKwEN8SovIvUY3i2h2PJ3KUjdAj5tz9doCCFllu3bLQV1NeX1r1zRuGKIPF/Z8mfrnQq3Lfi5u74FZu+U9sd+ItqZ6HfT9UkOWwPuBoW9d0vQx9/UY7a61Gxs3bOplPT2ozY4jdN9pbJolsDlIyUZvE62GvbqHw6XamsfXHQ+x/Y65jpSMW1MSEfyfrzusTYkS2nqaEg6YqwhMJG9oo7HVkJ17tO5Es/O4ZWbvC2DIOf/srT9+B4A3rP/9BrwcCWrxtVfu4bPM7J3ln3e9613yz/GkFyugX17bVqsSeoiT4Z+AamlNTLAjh0Uieb08xJXn7YmcCPfI2Yk8xI+OffFeN4LA+HV17nxd/wLELHZ96+kYslNe3801zZlgA0EaG3W4DbiQNTZq2Gm5rQ/jT294syN9AYMYJgQ1oJmsI/ZxJKr2uYgYmJTPRbVukMZW1SCuzcMgNiXDXLdc3vJ7xCZqQ3bOIkbF5U1FYBxtTdHyyMjOCamSL7PVFWl3rramNFFCUzKlRDQ7hR6n1C3aGh8xkpEdTMg1g4J7tO5ss7OuV95ZXnnHyB2+tr8o5y/OOb++/PPiiy9WN3sXFkd3iAt6Wx86jkT4Z4R2V+oytDvpIV4mwsRDXDt0cPTDSC7FYHW6WTRUtNZILnVj3PnaQXGp2x6BAaL0uLrLW9TS+khNsHWNhuJuptHjGK3KINeVDvmdaGFKY00NbwJNH4PYh3KMiOHNJUYNrbWjzPvhklBRDzFSNZPeMCTaPJjjFlYO/yYiGp672Y7stHU3KzSrCI2tpoHZ3OqE65uSr4EpyI6CGJ2mpW5N8L8hMBJilHCwOrITQoxWLVC+Ijt9V8755wHAzH7lrT/+UAA/tf73TwH41Y2/du8XG/YIqIJ/glZzQX5E/eEVoNUQhgq9JsIxowaSMx7QwLiIRmQSOqdqI7nXjWh2PLpZhDvv09gi+RzHOVUbXzMLhjLyAvoeh8WlrnAYn4j7TujQzLi8RYwamPvkWleix/UR/GtazLb0w9jzQthvgGHgNdWzSCNm778RLWatkcQwYMagIzuOa1r5c1ULtIdp1uuqtLsN2XE0RpFmZ0alKSkhqEpTsmpgGARGakqKtqaClIzBpuSRg8BEkJ1p3e+12Xky628B+AwAMLOPBvCBAN72mK+9EcBvB/B1t7726WY2mtn7YNHpfNWtr/0BM3utmT0H4A8C+Mon8LM8kSWF2jW2cg7VJQ4HN6H9MlqViGanF72IO3ypVtmnVHf0AqK5FD4CMw5DKO+CQWAi1t5d6lLXIU4/rL7PQhoYgY4aQQiehoC+V35PxIKbGd4Eri81vAnQkzktpk5P3p4XlHZUR8Dbfy6WIUtNg3gI0X3rlvcAkDDoTYnjbrYjRqpWxak76s3DUnfCjAE4a8G9HtTF63BwmpIxoFVhwjRLEyXRzQgr582NTWj6ppVuloYzvzPsyJliEb3R2M5RJe/ZuhNkPDP781jMBT4QwDeZ2btWl7U/AeCtZvYjAI4A3pzz9mn4IgBfZmY/CiAB+IzVjAAA3grgowH8cHltzvkHASDn/M1m9tUA/tn6ta/MOX9D5x/xiS1q8tVJKByiJTCc/Ismwm1pd4xBQYS6dJqSSze7zfV/5DQwt+t6D9tQfkSqIxp7XV2r8ppHj+dfv7yuihjVrae3ugGjBq+JiliRM+5QofdZp6EF51Ko75cT0F+igelzGH8aTV/McEUwagjc1+s5UfHnBYvY39RvJXtdcngTcYH07juzHWRkxzUSGIvxgYgYOe5mcWRnwjwccPbXsdHu+Lpzym6zs9HjhP1OcxH8V5CdQLjqTjfzkag8C0jUSjc7i/LhtqGCsN/ThNHyg9Hs3ImfIuf8GVhRmlf8+U8D+J1nvufdWBCbx31tfly9W1//fACfH9rsHV9HZvIV4XZL9DidPkBpCAK0u9bhqsp+NXpcPZwSiNGAppR8DUzEhWxy6BkIanamhEcveE3JgJcm7WHLIEYqTSXnTNHjItd3pxcxh9vWNKA+AvoQYsRY3vca3nS+T7bOR7pkeNPcAIK4vuN2X9fqmqtB3DWTz5PdzjK08IchxWWzhgC9vK7vLjljxIAoAnOmKbHl75SNBFw3tuV6qk3UIU+YcMBZ7CGARBXkoYbAbE2bQmPbEJjXni8bbHaex4T3VLU1pa6GwDxyXNMsgETNk4Py3bN112ls1yUubRIambAyLkMBK+fG1qecsPmSw4yPcKmHr1qDCsS486c5V1PiS92Ie5Eb/jmanndBNn2twz8ByGGlDGVpqRvTRAEODegCN8E6DSiigeFpS6HPsZOrAgBzxIWsOT2O037c3gNXl0fsI8MbpvkNubw1dtk8zQk3Q93yPsQwmPympOxX+SgfJ/++k2zEKGt2HCMBM0wYZXrciKUpOZcvE3F5yzlj9JqSUdfsLC5kM+Ya8hAwVFiaKAeBCRgqLE1UXfA/BIwPprSEf55F43DLoEDQGM2nNdj0qtm5rru4dleZtg9xJrk6kkvBTG4vqVtvSmLN2VizEkVcOO5bOce4895hfBwMOev79TUwei4Qaz0t513MfhO1uLG1pSxF6i61+c+x5iZIHMZDVsNK86C7m3F0s7ZGI2Pw/gC0DyulmtRQXUKDeIlrGvE+U4c37pAlaMnO1lWaPgaxT9CbnRGO4B/AjEMAMarTwiJW2TNhjRypW7QquYrsFCRK1NZYve540F3edivnWl29OTseV7pZRVszBEJQt2bngWh2rs3OA1vc5LbPhPUiBKbTRLiHUQNzuL29B2ZNRJhm1G2JoWcsdbX99kE0iFBR0Xo657zonIj9hlC+HnQ+iV7U2v0wTluqIw8BxHPyD+OxsFJieBOgCXLamsB+J4V+qGkFl7qNDVcIulnEZZM1MAF0zZmP7MRcNl0am+lNyZBmJNjZfJml7ijTzTwr54hmZypWzjWEIKDZOa7amiqyM+p0s1K3HtJZBP8KAuNbOW9NlILAFNOBikFBBDHaaWzXZue67uBirTmBoLaGya0JaQgaa3YI96JQ85AIrUpwEuq5pkWb1B4PcZYeFznke4JeNayUydAoX499JnzNjh4qyrgU6p9jTtMXF6QzdSOIBoPstB/exDRyAKdVCSFcVKZTgIrZ2AijWC7XEPCIyyYzvIm5bHJGI0td7d5D0dgwSy6bI06L5XLl+qr0uJwXwX8dgRmQYFITtWlKrKKfGnRDhWnOeOQgMCHEaLNcZpo+weVtmvGcOcjOje52l1YEJp+zC8dtgwJ+v2la6tq12bmuu7gkRCPi2kMhMI2tREN5F5myEgUCzQPhbgYEHop0XSWMkKdnqBNW3/jA5LwL1khAO9D5B0WguC3pnwk/BDWWCwTUP8fDYDCLuaZRCIzoJnj7ex+3dsRT3y+Th6MhqQwS1YnOd4GhQg/LZcDbb4we5yLgQc0ZY/UOBOi+xJBlqas937y6yUbcYJZ+b2Oe6wgMFuODUUCMppVuxtTVmp0S0uk3JZprWnYRmK2uZOW8IjA1YX7E+IBASoZBR6Km04LsWGW/e97Q1aDguh7IkqxEGyeZh+om5vAV4+S7D8Vgzg7/UGz9sA1odgjhbYz24Td9Kk0lpYyU6wevpa6m2dmoUIRVdmtHLyCmMWKskQF9z/3cBHmhe8yFzL8/RCiIHBIVMFzp5PLWvq5veR9B7E8TEWYcQOxPTojv7bo6AuPQZyM0NsKNLdkBI2ap7iGf6ggMliZKRUpcbQ10etyeA+MjJQoSddw0MG1pd1zdlXYn1E2nQjeraWtKcyYgMKUuodlR3NjS5DdR92ldm50HtvpblPoc7BDXv7l7nKBVUQ/57kMxsF/ioRjRJkwpubS7aLiqR7u7Ed9nTDAlgC0hnV0nEtnRNTv+Zw3AZdbT7p4H6TB+pD7H+vvsyDQPF1hP193Y4ogG49bYHCm5IG+oNWLPGncAqjaMcz9c6oo0NuIzodfltZia1sp/XmQbMSLRTd/ibuYjOws9bqKR9U2rUkNgsBoqQBDmp7zkyxBGAhKyk4prGoMYCc3ZlHBjdW3N1kQFEJhqSGcAMUqMZqcgO8J12JudK43tuu7g4qxEV9pHKLemrWaHS67WH15HgYOt1SXoGZH9Eg/FWJOa3UmoWjfnTNHj1MMB40AGRNAM/727/L1i3Y7W01tT0hiNmqTmoVP4ZwBRbh5WKmlVAu81yqI/Qo9ri9gfCcv7WF3eylk3iOHqSlbZjOV9yMXUv08mOyx5MWTdKeVVA+M1OwcckGir7GnLa/GQHS0E9TQVDUzl0LxaZWt1FySqFqYZQXYoy+VRbx5mpnkIWGVvNLZDTbNTDBUUzU6pe0V2rusOrqPwsJXyDRLf7Kh5F7QwVERgGEE6oBs10PQMUVfSw/qU0taI13fR4XDNw1KX2y8TRAgsXH/FKpunm6manaKjIGhscvI6pwcaRf3Sdi0qFEQzk/fMhKBeFjpcO+QHjBpWjRFleR9pSipNatTAxLO8j7no+VbOEdc05v4b1Y6ydXUjF/Z50fb5locDRkv09S10s6oGBjs9jt1v0exQyI4o+L9xQi+3uhJilFZ3s8phfNSND06TTwuLaIzSVremBdKtsvPawNQ1O7pVdr42O9d1lxfjmhZ6iE9EcnVQIOtqVYKcfCYHBtAFpz0QGMb6NEYTJOh84u9tIhpfQKcBHYkpPnDbupf7vbH6l5uwy5s/GY+7sTE0tsDQgtCdqUjqUtcX0EsaGOK9dolrGmd5HwlJJhDwxpb3IcQ+9XEhY+hmETdBZdikW/S3vf/mnJeMGcKN7SA0Jbs1ch2ByTbigEQPLY5TWuhmTt3iHseu47QI/qsIDIBJRIymaV7oZhUXMlihxynuZgzdbG1KBASmuJtVm6hAuGo++a5ptoWgCu58axM1XGls13UXV6/cGuahGJsA8siOLGSlD81qE+UcmgMTwCNxmFGb1JwzRefbH+Ja88By3Nn3GY1miIcOxiFrqasd8BkdxV5XMygoyAOjO4vQzTxzCbXxUyzvVaG7XzeApFJIlG4IolhPq/lIvuV9kJbrWd4Hmz56KCTS4xjLe6D9EEt9n7FujXlYmx2WxlYE/y6yM+KASRpiMQjMvCJGrFV2sXJ2kR0bMWbhM7HRzWpIyYAZA0YJgSnNQ62urgWaTwWBaasxmktTUkNghoDxwaoFGm6uyM513cHF0c30CaD2UGytrYnRPlofxgGt6ZPpJLSlKvd7K39/a9qdoq1ZXs/S2HwK0MvrqjQ2TrOjCHqXuv7EXaaxkeiZnjnkD0P2uhHXtPP7LX+lcrhlrsNOCwsYNRBIlEaPW9waq/kyIaMGwfJepvuyGpi2WpUILUxx2VSfF95nQm0m2ftOtsNizSwMbyha2KrZ4YdNi+C/qq1BaaJ42t1iPT3VNTDQaWzz5kLmIFEYRMSICNMM5ALttLC2VtkU3SxgfFDqDlc3tuu6i2tirERHfaKmPBTVSSj9UBQPSUwODKBqYHzr6ThNpa2hwn5QbI3AlIOi93vTDh3MAXSpGz10cDSg1kiUagEeOM8AACAASURBVHwACNQ7WbPDmjVoDRrjpGdmMqWPoTZGaUsAa5Xd9n4WM2oQLO9lBIbT7LSm5cbCrX3NjorYz6TlvWrcsRuj1PebhwNGJImWe2Oza1CQhxGjzfRwbLdcrjcleUWM6Pvv6YTRcp0WBt0quzQ7Vc0OdEOFEtLJaHYUbc2OGD1H1NXpZnUkSne7w2pTXUWM7tG6NjsPbEmCfxkpYQ/jWgaK5wYUtp4mc2B0xKjtYTznvFpPc82DrIFxrae19wOrrVHfZ7xORTx0JLIpkd3jeKqZaj3di3rHTpt1Sp+CRLUN/7zE5Y2h+2phpX3uv5zlfR8acYzu2wdZZ9zj1OeFYmCy1CXvD+T9DCuyo9DNDlRTcpDCSrcwTYZuJjRnJZyyGtKJJb9H0QIVdzM4h/FZRIzyxDcPUhM1FQ2M7x4XMT4Yb9paZedCY6shUfdoXZudB7YUrrTuKsM9DCROPjEJjQhZj3Oic2BaP8TVw7hysAV05MG3ntZoQPtUnKXdkYeDyZ+2L3U1LQXjQLbUVZsd/vemUEaX2rxBQcRCnpmOqxbyTF3VUKHslwv/1LV3Vcv7AB2KouVGjBqo+6+O2EsW8jIC3sG1UnHZFKycl+8jh0Lifcfbr6rZOc3Fytk3KBglLdCM58yvm+xG2u88vWf5D7euFq66WTkThgoSYsRodjarbL15sAOD7CgIDLHfgFV2sakebyr7vUfr2uw8sHViHga9EI1uLmRR2l0HzQ4Vwqcdxpn0eUDXwDDOUC+vq+2XFgqzTR8bKirT2NamhOXki9eBoYTlDFrQC9xCNIg9R5Addzo+aqYK+zUmKH1iDgxQvw6bVfadcCEjtB9lKCQiUbRGTjKA4PNw5OeFpzESmz6ebqbSUTXDFZ3G5hkU3OBgiW4mT5sbm4PsDIdVW0PS2E7rAdtBYBZ6HK/ZmY+E5TIKPY53pWNzYIqhArsKAlPV1qBojALITq1uwCo7r3SzkTEokJqoo1/3Hq1rs/PAFkWjiGhgCK50NCSOFsg2tp5WNTuFbuYdFNWm70RMrwF90kzTrMTDzHFDYBrvd2LRDJEet01YWQ1BW0vrGG10fU8QovSIZocZiKhCd8BHz1RKX9lDTYNYvi7fd1gNTOPcsJgxCqEVDBg1TCn5DWrUZZNs+nTaaNumrxctlzUwKZSomQx8LDk7VU0JihaIR0qKpoRBjG4EzU5icmuwGyqwn2PKyhlLU6I1D5wwfxYRo50WVkOMdKvszUighsAErLKvyM513el1IuhbodC11J4rDayHA3q/ra1EYxbGrcNKex2aaTF6UFvTer8snUS1llWtsvXr20c4DrR3TVN0Ub1c3lQL+UfjUKWblb9Xt5Bvr4FhaK6RnLPjlKjGV617mjNBc9U+bzlnkR4n3h8aN3200Uh5XtC0MK5umboXa2JvnaYZj8zX1hSkhEbsTxwCk1ZDBXZIuNHNCIOCUdAYJUZbgz3HiEXWd2SnrfEBNovoWlOiW2VvTUnV5U1HjJBKs3PV7FzXHVwnQpgfojsI9LjWxgcRzY4ikG0toFebMz5fRqVRcIiGGq5KU6FUzc5Wt7FQuFPTp06apck4aT19GMVQUdK0QjY+YPcbCEH17jtLXZ3O5+11s8pWERj6c6FaLrdtHoCCcJFMAMHdLBN0M7Xp25DfxhbctOW9aJXNDlkKkpJYZKdYI7s0thvJ0nomERjVKpttdhbEiK+7IRpOU7JZcJORAntdAjESaGx8Xc0q21IxKPC1QApiZOv78VBrzu7RujY7D2wpD0WJ7iDQwppra8S6aeV2t7bKLgJ6OslcPeQ3fojv9Li2k+Y9LI+tqxk1sIcv3oKb19YA7Se3al3gdqhoawSGRyclJGryLe+3uuL9wXWzwnKNVdc07xqYGQ6DdbCQ1zWT0v1XrttW46lkRC11RU0fbcEt0s3oYVPbYUgR2M8zN80vAnrPyhnDAaNlTGTdRCI7KIYKdFNScmsIxEiwymYRmCQaNWQmpHOte5AMCghkBzpitGl2auGfAeODEkB6tZ6+rju5GHpGeYirE0Cf2x2jUbAaGNXyk9fWaA/b1odxlgIU1eywk2ZZC9TY2puxGQYC7wfS5U130StNKmksEaCbMVSgiOUycyCXc62IpiQSVupqHhCz4PbuD0AxgFCHQm1proAWvsxe35wzWVczMFFzrWiaFRtmLF5fnpYba/q8/W7IzsQhO7PQlAC79bO3NsG/az19EzISGCjEiDc+SDNbVzM+oLQ1WDU7mOkQ6k3w79DCVKvsDdmp0uN0q+yC7HhI331Z12bngS1msghEDgf+JDSkTWBCRYNWzqwr0tOmLdGUpai2xr0OmlHDRvtgm1/yMHOU6XHqRLiP9TRLP+yRW3OQc2tYdFLX7DDNw80wyMMQV/OAZdqv7pejx6n7Jaz/Tb9PMkiUSmObycO4jMCITYluuMJqa8j9Ttr9V0bsPQSxIDuFRuasrdlxmpLy9XkiEaM1TNNzIcOw5uywTIutbh3RUMNKC2I01BANLIiRQrsDjeys9Di2biquafXroFpll/DPQ+06BKyyN+e2a7NzXXdx8Q9xPUejtbaG5XZHrURbIxrsQVE9jO+UpdZ1VxpbYwRmR3baTrDZya0qHD+R11fN0WCRqG2/ActlJmhWRTQOQz1fBoghJcx9Rw4rTcl9nwEL1VUzauCaqB4ub8NgGEzX1rTWwOxDFvL9q1o5k0OWfoYgGhPArStaZbNNXzlMJprGxoVpqlqg8jpP8J+HA24EullOnJVzscqm3VFLbo1raX3AaIluUou25uBdX9PofDSyI1plF2THNYAQrbJLXRdBvCfr2uw8sMVwu4FySGpLzxgGgxmvTaDT3EdtonYkJ2qHYF2eTtIW2dEPMxrtrh+iQSJG7KFDbVLpCXas6WORqAiyw2QkneZMUynoYchougaGaB5Uzc5pyi4iB+i0u9Pk58AAy3uxtbYG0JrUYnnf+nMha1Ua3yfV4Q2rvZPvk+RnTR3msVlZO7JDNiWncritW0TbSl2aSMQoFytnD9kxzSo7kUYCqlU2Ji4HRrXKLi5kVStnFGRH2G8qCIxTFyNGiW7G5SNNohbI0hXZua47vI4kx13R7BQrUeohLtBqeG53DIFhc1Vk17TmoZeaa1prJEq+vmz4p8jJP4quaXy46opw0fkcLD2OfZ8F6J2iXTZ9Pkh886BrYMj7TmOaa6mrurx5yAOga4yU+y+LrPM01/J5a3w/k3OtNISWz+Hi9ttPgxg0iPGa6rXZyWTzsLubeZqdtS5LY5tIQfqo0eMyaSSgIiW74J/JG+IRo6JVGRwERjU+MDKks9RlB1iWOW2NSo8bE+f6d1/Wtdl5YIudLI4Cx73QzXgBsogQ3BNL4P512x46ek1YWacwnSbY26ihbZPKJq/HLNkzRTfbKUb8745BNCIub8z9IdJEtb6fAYLLm6jZUYwaeDoUh9COBVlvfT8T6VsqQivTzcg8HH4YwtHudNdK7vdWEBqWxpZIK+etKSHrYjMS4OhxLBIFMqQTq7aGRmhL3dYIzFr3xqmbbVzofOTnoiAlvnucZtTAGgkk2fiAQ4zuy7o2Ow9sMbkJwPKAa83tBpYHkXxYbGyVTXO7VetpNZxSRB7YQ4ecH9FYA8PSwqKHAzpHo3lTEpvctv69AatWhTw0Axqlz2smS101B4Z1TVOsnCe2iVKtsgnB/1ZXsLyfk+8uCaz3X/l9xiJnrYcsmubsKAr+6fskqb3ThxbcfqO0XNfivGh2WKRkZt3YShPF0dg2zY5zGLegFqhqjYwlF0ixys6b4L+tVfZGY/PycFbEiH2fbRoY1+1OQ4w20wGn7mwaPW7YEKM6XfK+rGuz84BW4XZ7VriANllkud3AKhRuzO02M+mQpKTEL68XD/ketzso6GVd0+SJcGsNjNj0yU1qcw0Be5jRUBLWMlydYJfaLH1reT3/u2OaqJtxQMqgU8cZy3ugWDlriBFHjxukHKMerpUn8nBb6urNOmuooNLY2roU0lbOnYYsUWSdtZ6m3diKQQyZs8PS2IpF9egcxgtilEkEJrMW0aJVNsgwzWKPzCJcpa6ngSkhqDQCU5oz1z1uqcvSUVmkRLXKLnk4bo6RaJU95iuyc113dLGH8eU1fFPCcru3urIGhttva263qtlhE7x1zjjH7S6HcfbmqtLj2mtrtMMBnWQuamBYdz7dqEFDohRDEKV5WGrzvzsWgQFAp44rzZmqXaLvZyLdjNfWiPezxvtlg2ABzdpbpeXK9zOaPvuUkXVWE6VqMWlkR212OCvnkpfDI0aclXNpzia22WHDKUvdE1e3HPJ9xOggWWUXIwGPJliaEnbIMiQOKVGtsve6HmJ0wA1mWt/JIkb3ZV2bnQe02AkVoGpr+MmiwnEvhx5e2Nya261yxgvy0PYwznK7o4cO3tpbbB5cbU3QqMF1jxONGkga5nZ91eascd4QwIdpqkGS05zo4cLyev53xyEPYlipYGlNhxGudLPWGiM2ZBYoCJdq5MIh6/L9gbTKVjUwLFKtW0/3QdabMwHI5tdE6+ld8O9pdlakJGnaGp8WpjVnpW41BwYLjQ3g6XGliXLd2FarbDZ3iW0einscOwxhERjVKnsg83AWehxv1DCmExJsQ9zu+7o2Ow9osRM1QLM+ZSdqgEb7OG4ITFt6BsvtLlbZaq6KPwHUOO487UPU7HSznu5k1LA1v23ryha7ohubrw3TDQpYK+fIdFxBjBSUlqG53oxLU8JbZSsaGPb3xmkFAU2zw1qyl9e0RjSWugJiX64DY1ghPC9oN0zV+KBTfppK9+W1mFzzuyM7pEHB+rrROdwOmxaIpLGR7mZyczZzYZq7VTaJ7Mxcs1MO6xPZRNFNyaCFig6JQ0pUq2zWNS2LRg1jnjDjADgmOfdlXZudB7QUZCdCN2MPSa21NUtd3VCBaaIitA/WqlWl3fF5LaLlsvOwjVqqtnZ5U61lW4d/qq5ppakenUPzjdicAauVM3UI1dE+bmihO95R4Z+iVTZrPS1pa4T7mWI9rdzPJM2ORE8emmvOymvU+6Q/tBBpuancJ9vSclmtoGyVTTa/Q6E1JTIPZ2Y1MJrL296UkHVpGlup6wjdB01jhK15cJCHQbPKxkbfqu93obFNvPU0i8AU2h35Od7spAlkRzFqGDFhtoeB6gDXZudBLaUpUZLB2RyYUlenZ3APWzUsj5kIa/vVmhL+IS4KZFvTwoIC5ObJ6yLCpbvzteXkFwcyzx5abVKBolXhDQqUa8zmZSl1T2yoqJCRVCzv2f3SuTXC/UxxlzxtLmRt72c7QtDYUKFT08cOhcrbmw6hnlSaa9vnkGzUQCLgg4iUlEO+ZyRQvs4iRhstzEE0dqtslsa2vO7A5vew+yWbB9VQYTMScJodDMUimm1KuOZsM1RQaWxWf58lO+BgMz18HPKMGddm57ru4NK0NbpAltHWaEhJmXy1nSzqhgptmz7ZNY3MrdkO460RDflwoCE78qGOnAh3o90JRg0KJUyyXKbzWgplkv/dsdbTS12t8fOWoreS7mcBrQpzP5O0jYIb24KAa5836j4pIOvK9VWsstkQVDPbaI1cXQ1Zbx2CKltlk01U0d7kmbMF3tzYPCOBjR7HIkbL3z8+4gwK+CZqbc5ILRBrlW0zRwsr9LiZbM5YDUweDhgs4zRxvzdW8J9Fq+whT5gIulleNTsKjS3Zw7CdBq7NzoNaElIiHQ5UZEd82DITy1GxVNUOSa1d6YpVtn44aPsQP5JIVPdwVfVQ18HFaTCfbqZqdiYSfVEpYcDSAHPDBf13xx1u+WuRc+bd44T9ajSrYUWChLqd7g+sUYMcttvYtVIZCoXu63QzyX3e2PuZfH9gtYIiUl2QRu/3VmhsmTUS2BCY+kFURoyKBsY55JuoBTKSbmYijc1YpGRQ97s2Lx6NbdCaya2u6/K2IDuJ1DUOmDERdLNFYzRRDps5ZwxImK/NznXdxaULZDUuOjtZlA8HjLBZOBwcpYetXpenx3XSwHQS9Oq0xl60D48ep09uKcqS7OKUKWpRSLPDIjsBx7vWmh1FI6fouLbPBUnfur2X2uplpS8hJQJitN93uKZP3S9laCPQBHsZNdAW8qrRSCetIG2Usx5+jXZNW5ESF9lZD9WJDJJcQyQPN57LW2nONHqcd8hXNUabZserO2p1rYRpejQ2K/Q4ru7AaoFEq+wxz0gE3SzZSrsj6k4p44AJ6arZua67uPSJGvmwLQJ6RjAdORyQwmaVlsDT+dpaLpfXtNfWqBoYDjG6Uet2FvSyrmk07S5xjl4q7e5IZsBENDsTbT3Na2CWuqzlMk+92z9v/H6pJmpz0eOGLGxdbWihu6axNMHW4Z9b3Q75PYq2cfu9NdYYKcj6YJr2DhBCksmJO03nKzQ2silJmwaGDBWlkZ2SL8PS2FgkqiAl3H7ppoR0N8N6YGcRmJFFYMT9Lu5mg083G0bJKnvME4XALDQ2DjGa5rxk8jyLyI6ZvWBmH2ZmH9RzQ9cVX+zNdXlNgCtNTupau9UAovW0SLuTmwey6ZMPB8TD1ozX7BxJLdCODrB0Eq5JVZGdQjcb3HyO1ahBmLCyU3yl7kQ6hUU0OzwtjG9Uc86YyMZPsZ5mrb2XurzJBhv2CGhNn3Y/MyFYVdivQCPWXCv1uvR1IHv1k9KkCtdXaVKX5xBVVsgjW50E5SGWh+wsB0oTEQ3Pcrk0DxtS4dYtiIZDN9sQI7UpcQ7OmytdW6SkWEgnUhNlbF3TjBpowX9xjyOuw0I345qSPIy4IbVAxzlhRHp2mh0zG8zsD5jZ2wD8HIBvBfD9ZvYvzexLzewjn8gur4ta3bU15ASwj/W0IhQuCAGheRBod+qhThG6L3tpayzBXl9VmM+G5UUEvawzFCDQ+RKHwOz5SDz9hT14LXWVZod0YxMQDWWSH0FglLrMgXFz3xKMGpi3xIZEMYjyaMiZ2y87tABE+qzg8hahzzIhqBHXSub3NnTSGA2D7sbmamssqG10hmPDoDYPJV+G09awyA6LlKiI0VCaLUdjpCJGW26NW3cNV1UNCjzESNQYDQICA3AaoykVBIbQ7NgSgsre028wIT+QQFHAR3a+DcBvAvBfAXhdzvmDc87vC+A3Avh2AH/ZzD618x6vi1w7B7u1oLfPxPJIcpoBETGauIcMEKNRtKfd8YdFyQJW5aJ3on0oyJkyxWcnt8c5c85bptFUlAwYpS7AN37lAMZpa/hJ/iA0O8r9YRDeE+z7DADKX90aiRqFA66CRI0WsGQn9yu7S7LIeofhmGSooAzHhgHsbIGlSx7EzzFL9y0uZRui4Kzimmau9fSK7NDaGg7RsA2BaUtjK7QxVgtkYlMCAYFJGJaOmajL0tgWDQyBlAw8YnSaE11XCUFdNDsPi8bm/SSfmHP+6Vf+Yc75ZwB8OYAvN7P377Kz65KXMlHbsmDm7E72VA0MrR8QhM2HAGLEujid1AkgTfvQ6HE8PYM/jAOEBkY85B+nxb7Yy5cp14l1leHF8ybVneYkfSZYmso0Z7xw40++1LopZaSs0beopmTT3rVFdtTDLVt3RzR49Ix5D6sGBQD3XlORKOVzAbRHdrQhy4CXSCG2QqceTM9PY5rURbOjuby5eTjlvUu7vHGI3DCu9xAagSl0My5nR6abkW5sNLKTOMH/hhjRgn/ONU3NBVo0MKOv8RDqprS4m1EIzPrzzASyc5ozDkLdwTIVrnqcEkZLyA+o2an+Ph/X6JjZ68zsN9x6zc/02Nh16UuyXFYe4uJDsddDnK8rTFgHEzjYfegkx270Q/JhO6yC3sZalaIF4gXepHherDvNmbZjBzSaiiL2V/NalMaPakoUhCDgxqZQECUkqvV1EO6Tksub8HuL5Jw1R9ZJK2dAG970ykeSmtQxYu3dVmNUml/X8l5EdkDSt4ZOGhgVMdqaEq+JGjTEaMyky5vY9A15xsy4kAm0u1NaaGGUlbOQYzTNCQfMW4PE1GWas4UeN3F178miDArM7BvM7FeY2YsAvg/A15vZ5/fd2nWpS20eAC3vgnMvUg6L2kNcD3NrrAXqFNqqcP0jdb2H7VJXCYPlKFbKFB9YecKkk9VSt48WSGkm2WDKpW57zYPSoKkILVtXyq0R9FYn8TAOcA2wmkcGcIMARXunaYH4Q36v/J6IxpPar4DsKPdfBTGSkCiRdncz+gi4FWSnMd1sOGiHfLpumG7GGR/wddcmyhy3u3LIZ7U1dFPC5xhNc+YF/9t+ffe405wXhzVzGj5gQ2kYZEdqou7JYt3YPiDn/K8A/B4A/zuAXwPgP+q2q+sKLSnfoJuweQgIehs/vAQERqvLP8SHgKC39cN2OYwP7sN2qasdxhU9iRKCSumsVKEwi0QFBMiK9XSP5nfX7DDNg1A3gpQQjWqkLkNHVeoqTd/+HiZc3hTraUULpCBRphh3aEh1a2v6Uld3u2s7FDp2GmKx98nx8NzyH5lzCxtEGhtraT2wNLZNA6MiRhw9jq075hkTDq6V89a8EU3ULAj+lSb1NCfcYN7MB6pr4Js+pW4WkKhjaXaeFRrbrVXepR8L4B/knE8A+NCI63oiSxP09hE2K2JsLSFdeShqDy9eA6NNmnWhcGv6C3fIX+ryyI6aAaM0k6zOChC0QBN56JA1O5pBAVtXO3wVuplfV5m4a5qd9RBKWrKzdZV8GUU8HkGiFC2Qch0UGnF7lzeNzidra2iXtz5IlPI5NuMQcI32zN3PimZnoBGYQgvj6GYDSQvbkBKviSphpo1pbINofMDmyygao9N6yKcQGOMRo0VbwxoUFDc2AoFJGQdjaWxLXUYLNM35mUV2vt/MvgHAxwP4R2b2mo57uq7gOgUEvZR70fpQbC9sLoJpTkMg5/e0tmpVBcjCQ7HHw3ahUXAfcVUTxTp6mSnhfmRTomp2UiLtwlVkR7SeVrUJEmrE08Kaa2ukME1eC6TRZxXtUkEe2u5XcnmTtEAa/VDP2Wnsmqbmp6kh1B2QKOU+yYdQc3XLYZzVwGy5OS6NrTQlJLLD0s02pESt6xkJlKaErEsjMCuiQSIlI1kXJQyWCCud0ppbwzQPW9PH0NjW5sxzpAN2LRBxHaaUcEDy9VD3aLFt21sAfByA78s5/5KZfTCA/7rbrq4rtE6CNadmWcs/vBRr2Z6IBtBes6PQPjRtTcbNwNHNpOswccL8ra6QFM80qFtdYdL84nP+LUlFjE5zpqfMUl3VelqlAXVzTWut2eGHFjEtEH8dKM2OgGgoZhiS0F3QAm1DFqZ5GA1ppRF74bxq86tbRHO/N7J3uNWktm36jqRWsNRl93sktYIFSTGWxsYiMIcSKsq6pnE0tkEMK13yZYaNuunVZZCdOWUcSCOBrS6prbkhkZ1hbQozM2iaVsE/00Str5mJpq80Oy8JhgozcR2O04JEeY3vfVrUiSXn/B4A7wDw76x/9EtYjAqu6w6tHXkQHraNtTXb4Ys5HIguTrQWiLRcBjpan4r0OLYpkcL9yDDNvW5begZQri8f/qnoVJprawR9xpJR1d7RC3gSCEFbUwXFkl3S1ojCcb6uoL0ThkKS9k54r7EWxoBmI6+60rGIhmIZPkhIScI4mNvElbrKcIFB64Hl9yaFGTP39fVASSM7rJHAICJG4LQ1qvHBmGeObrY1D9whf7QZiZjXb6GiDLKTBBqbgJScUsLBOATGJDc2gW5Wri/jxjZPGC0jM4jRPVmsG9sfAfDXAXzB+kfvA+Arem3qumKrlwXsURCGKpodlTPO1j2Jk3HlYTsYqIethsBwFsaAHu7HXAO17pT4w4Eq6JV0FBJNhdcC9UJJFPohoFkuM7XVEEmA0wIp++2e3yPcHxSXN6quiGjQdSPDpsYubyF6HF2XKrvSZ3kERtF4sjS2pS71Up4eV5odGtnhrKfHQxHQc3VH2o2t1PV/cTlnjHlCAoPArAdr4joUYT5HYyvNGVM308L8Ddmhm5K0oTa1tTdnPLLj/c4AwFbXukQgRvOJM8G4T4vV7Hw6gN8K4BcBIOf8YwCuYaJ3bB0DhwPOsjZCfyHcizofktg8EZpGIXK7+2hreC1QocdxdQXNzpQoy2W5Lml8oGiBcs40nWQU0E6FgqkiO5r2LuLqpdwfGE0f3zxoeTiK9qM0qgIS1Vy7pAybhBwjKeesD61RMxLg6WYK8qs0JQpiryDrisvmkbyflYMq3ewk0t1s4JsHYNXAYFisOWuvE1zeprQ0Dwyyo4SrTkXwTxzy96aE2O+mgWEQo9WggNQCHTBRSMkegsrUXZszRlsjhKBOq+315jj3ABbb7Bxzzr/8ij8jrUOu60ktRRiq0TO0UFG6rnCok+g6skVp+4etZn0qamsUZEfZr5AMTiM74yBpgVpf3/I+bB3+2cuBDOgYKtrrMB4YWkjNmaIForQ1GqLB1o3k93B1BSRKMNnQrJyHTQvkraNAN1NCRdlhiFqXdVUENCRqIo1cyrSfbXaMFPzv9DheCzQTCMxY3OMILdA0Z4yWKARm09aQyM4BicqX2Q7sxH5P634ZWphtGiN+vwrdjGv6ZtwYh+yU91km3sAF2XnmaGwAftbMPhJABgAzezOAf9FtV9cVWpt7kSBsVh623CEJfN3IoY4K91s0JYzg/7ag11ssHQrQLVXZh62mrcld6rLW03pd8nAAfiKs6FQk045Atozi4gT0GAIoiAaEuhH6LJ9b0zy/J/Eub1ouUO9hU2vjGcHQRqDdsRbygJ6fpmhrtCaKrCsgO9OcqEZdpbGNpJVzCdtk6uacccgTJ/jftCp+3eOccENaLu+uaUTzkArdjNkvj0Sd1v0yNLbSRHHuZry2ZkeMCCRq4pz5btedif0W9MccquR9WuxP8l8C+BsAfq2ZvQOLQcEn9NrUdcWWRPtYD39azgM/EWbqxg513CGJPuTfEvQOE1EPngAAIABJREFU8NyLVG1Ne8RIs1TttV+BzmfcfnPOmFKmGnWAR3YUsb+EOoh2wACnfwFiNCtOs6No7wa6rkKfVbRAIfSMuZ9NOrLDHvKB9vuNIFGtqZi3694450vWkr3UpbU1KUuuaZqmT9ivgtgz+y2T/MzdICxPSGYbPet8XQUpybQwfzzwtLuNFsbULXQ+hhY2pZXG5qeglKaEQbiK4H+imgfBUGHdL9WUCE3f3uww1tNFa0XQ2E4rje0BITtUs5Nz/lEz+60Afi0AA/BDOZNjiOt6YivysFUmt71oKtLDlhQgKw8vgHyIJ+0hzmqB1JwHPpwyC9qaAScy30BxY2M1O0qjrtRV3rtFC0Q1DlLmEo9mALeGC9Tngs/w6WU9rdBnFS1QL11UKCRZ2C+X36M3JZwmSjHZyDwCLu63C1I9kUgJVDfMhNc8x2kTBjMcW7uxWaGFkcgOZiSMPi1HQIxO8+oWptDNaM1OQiYsjItVNqcFSngOiUN2Rh4pKW5sTFNS6jJaoNN6HZimxBTjg1NBYPi68+Tvd0d2Hk6zw7qx/e2cc8o5/2DO+QdyzrOZ/e3em7subWkPW13Q20OAfDOyD1vNKUtpHui6k/YQp5GSxLumSfk9gvV0N+3SyGmBFN3H8jpOC6Qc8AH++mpZInxDAuj5JwBJCxNCJPsJ3RUtUAA9I2mugObGxtFyA/owpS6pgQH4ZlIdCrEW5wqirLi8KW5siiEIfZ9Uctk6uLGlVOhmvEaDakoEZEdBjI4FgaEMCkQkymZKUzKOKmLE0c2GLVyVa0oGy66DHrA3JczvLa1GAlRd4fqm6eHR2FjNzhse82cf3nIj13X5Uqg15YzW+mE7Kg9xMv8EEMP9BHcdRdCrPWx5Qa/ysFU1MOwkdByMOiimlJEy3zyw+z0Jjl5SXYFiBfBcfy1LZPm3ookCNPctCtkpeybeE1JdicaGQN0+BibN3c0m/jpIuUDrdRiJe4+mO+M1fZp2KQkIOH+f7IWAs/leS13eDZMPFS20MCZzKWEEJ/gvdQdwiMbINjuCocKU8rJfqSlhBf8TaSTA082Ktoajm/FaoLk0JQJixLi8zZtrmpA3RCBGc2l2Dg8H2aleITP7wwD+UwAfaWbfeetLrwPwQz03dl362h6KzEN8vQkrXHTmYStZtbIwP3RBryJ05+tmPCfQKABOC6Q9bIVwPyEZnNUCKaYSQNHscA9xgEd2WC2QgpIAOrLDUTtFZCfxjZ+k2RHCdpW6Wr4XrwXSXCB5DaJipb8hUUzdCBJFaiYfjQOFgKuaHYWWy9cNNFHEffI4J7z2OW7SrGgbj5PABDD+c0wbNWw0Ns7d7IZGYHitipJbs01vKMQo4TlMJN2saJdYjVHCiWgeRiFn5yjk1myaKQbZEbQ1uxaIaUqW1yg0NgoxKjS2Z0iz8/cB/AiALwXwObf+/BcB/F+9NnVdsaXkG6gPL5Zu1l1bQ9fl6Q4Afzh4kXzYKlqg46TRzXgtkEa7k7Q1ivHB1NasotRVDrbKfhWxP+Xotf4K2ImwogeKaGta53Dt+21bdxKusVJXCUmO5Ri11gJpropL3fbaGrbuFKDHMfdJBYlSjA+0kOSBuu/MBQGX3NgY852Mg5E0NrUuOFqYgkQd54TXGkkLE5CdYnygIDtMEzVNM0bLVFNSNEYK3YxBYBRNVJp5I4HN5Y1AdvLa7AwPSLNTvfI5558E8JMAft2T2c51XbJ2znh7Qa9CswK4h/ic+ERs5WE7J8WFjNcQzKnPoUOtyyAlhW7GP8RJREOkhR1Gw7uPbcXopa5km07XHZqL581M1lote+mjrWltqiDRZ4X9HiVkh9cgahbcvBboNCeYcQi4lAskhA7L2hphyFL24q3TnPC817msS0Xse4SKTqxrGhZQQ2p8KWTHMGOg6GbHktfCIDAC3ey0uaY9J9Rlwz8TZiYPZ+AP+cU9jmlKRqF5KLSwTNHCiqECn1vDITC88cGureHDSinEaHVjG54VGltZZvZ+AP47AB8F4Pny5znnN3Xa13UF1qwIkI1/iCtNiRbupwlO+boZj0i6mSboDVwH0saYP8zsHPdaaN9OWeKbKOa9UH4evvkdtLqNNTt749D2kDSJCJfkDiUgtJq2hkc0Ipqd5q5pipW+ku8VsMpm98v8zm7XZfVstOZMROzZ+6Ra972e74HY66GiOecqIyHnzGtrsLwXW5trAEDCgIFBYBIv+Nfc2DKex0y5pkmI0brfmapbfqY+rmkUsrNZLjN1+bDSeUVKGCOBrS5FY+ONBJQmqjRED0mzwxoUfBmAdwL4QABfAOBnAHxjr01dV2yd5oyRtBLVkrb70B1OSkicUreTFkhBuJT9Ks0kG+5X/t5ROHwpB1Bmeg3wHHclQHKpyyFcSqgoENDsdHCHUgwKFBrmVleguTLUu1k42GlaIKUp4fO9NiRK0S6Rdfn7JK8FOomuioCibexD95WbM3Igomoxve2Wn0fRGClmFezzLWEkc2tW1zTiMC4ZH6zW0wwtbK/rH8aPk0CPKyGoVL5Mwo3NZPPAu7EpSIlEN5MQmOU1jFV2XusyCMwgIDul7viAaGy0G1vO+X8E8J6c898B8EkAflu/bV1XZClIifIQnwVOs/IQV+hbygRwnjN9yFcQmFnkjC916w+anHPsOjj7Vab4pW4PdzOW4x5BdpjeQW7OSM2Oiuxo7lA6AqNMmxVamITsNNfA8NdY/RwPhioyGqkbQqobu6ZpSFQfum+EHtcc2SE/FyoCQ98nRSOXZP1obAMIutnqQpYpN7a1KaGRnbQHWtaWgEQVwT9nPc2Htu4ITNtmpyAwjAamIDuUFbmQhxMxKHhINDa22Vl98/CSmb0PgAnA6/ts6bqia4rQzVgEpgt9S2/OWA2BQt8CeDcruZl0thsJ0wT8h7g6sRxJQe8caEp6IEZL3kVbfcZeV6GpKMgOG0bIo3KK05uCRimT/O090Tikc6/buunro0FUrZEBXgsUEfx7S7Lol5uo9kwACdmhh0IaQivTXMn90jS2OdOWy8U1jUZ2ZIMCFomapLqckQBPNyvifQbZ2bU1Cj1OQIwElzdFs8M0USaEq5ZA02fGoODW+qG1yflfAHwHgP8PwPd029V1hdYk0MIkQa9AC1Me4tOc8fyBE7LKWqAOD3EF4WKRHeVA9/K6zsRSoCyV1zEcd5m+NXJaINnSWtQCSdoloXFQECM5Z4eovWf4KJQ+JhsogMA01uyEtECkaYWMlJBGAj0QmDll6X1W9uKtadbr0k1J41Dn5d7ED29YxEhFaFmXN8X5EACSjTRS8hoytwYAJowYyKbkBjOOVBMlWFpPMx6RdLPN+IBBHk48ArOhSlSYJu9utiFGxGetuJsxGpgNTSH2m5NAY9t+b35zhrRch/EBITtUs5NzfvP6n19iZt8F4FcA+HvddnVdoTULkzpF0BsR3vLNw9OdAJZMIq9uzllCuFjah5ovQ08sxSbqdt3aXlQkaiDzcGSNEasFUpsScr/q5FbJ/dDyWsr7jKgroGchLZBAY1O0QEq+F0vLVQ/5LBVTRYzYumy+155j5L92SqmLocIUQNZ9uhlv6/2yus7zbRLvk+NoyNk3iFHvOwkDRhYpsZmjsa11B3BN1MgaFGz0OCakc32N0kQRTd+eAyMYHzCIRuJzawrqQSFGG1LyyK+70c1O7muLjfRw8OvubmwCskPUvS+LRXa2lXN+W4+NXNfl65SS8BDnBb1T4HDQWiCraAgkjrtxzZlO3wJVNzJZXOo6iFG4bkYNbFPpcU9bC6SGio6D4Tj1MD7gk9eVpkSjWfWxtNYMChQt0DJcUPK9WMRIcd/i60Y0fVzD/iIzGQd/fyh/N00bFWlsOhLF3SdbI1yKzTvw8ufFo2qzo91/k40wqnlY3M0mpnkAMJtmfMBYOSvGB0kI01SMD9KmrSEO45vGSDESYMJKecQoz0LOztac+dfXtjwcZr9C07c1O3KLcGcX9Uk0s48zs39uZkczm80smZl/xa7ria5pjuQmtBWySkYCAVoYXbfxxFKnQ3FJ8SotjNYCRes611fluLNaoF5IiYIOAIJmZ9ZpbCyyo1D61FyV5XvaCv5PwjXWNCV9tB89873Y98OOpD5FulmHsNKS79VaY6TSwjbEk6Sxqfv17pN7wDeL7IwYac1O4poHAJlFjIpBAdWUrMgOUbfk1lB0M8H4QGlKFOODvSnhkR0KRhVCOoebQmNjwj+X1zB0MyVnp6BK4wNCdliDgj8D4DMBvB+A9wbwXuu/r+sOLUVwqtPC1LqkKFKtS+o/WluqTuqhWZxYtg732xGjtvtVEa7beRdU3dZaIBExGskcDSVIc6nLa3ZmYYqtWJwr7wntMK5ojDSaq0yfpe4P+v2sNd1X0SCeZv2+ziJGKi3MR0q0+w5r/a/Swvi62n2dff9u+yWvQybd2CTXNCw0NmNobNOE0TLnmmaGBNOQEsnSWnEh4+sybmxp4mlsuxaIb0oYpGQsBgUUAlM0Owo9jmjO1r/7WTQo+MWc8zVX546vEH2AfIj3EfQKyA6ZC6QKWcex0FS85kFz9VJd05TDOFM3ihh5DYQiRgdefuioa4FE7ZKoBaIRI+MoQHNHZCdihys5yAnIDqUFSkK+l0hzZQ+LCvKrWS5r2poXHrXXAimIkZRjFECMvI+G2pSowyZ6KERqMdV8L9n6n3VjsxEjsh8WPSfcYOIE/2vdgbifbXQz8nA7Y6Sas70uj8AwiNGO7DA0tlKXaEoEwf9mqEDsF5tBAdGUbFogv27RCzE0tq3RIpAdS7yl9X1ZLLLzd83s47vupLLM7B0rje57138+Zf3z9zezbzCzHzGz7zezj7n1Pa8xs79pZj9qZj9sZp9062uDmf1ZM/ux9et/9Gn8XK3Xguz0Epw+XSGrOgGkD/niBFCeCLsPRfXQ/PL9nFuRHBiqrshx74YYifQ4/tA80Ad8QLOe5nN29EZVCensoQXqQQtTaLmKu6RCY+uXs6NpEFWalVc35xy0nq5fX/WQzyKT6udNRXZah1ur9NlsA0bMVFj0iMQjO8Y1JfNK32LpcQmDZCTA0dh4g4KClCg0NgbZkSyXlbpJydnhLbizQDcrWqBM1C3NDmUscU8W+5P8UQDva2bvAvAeAAYg55zfv9vOXr3+45zz97/iz74QwHfknD/OzD4awNeY2YfnnCcAnw3gpZzzR5jZGwF8u5n945zz/wvg9wH49QA+EsDrAHy3mf2jnPM/f4I/T/M1pYTXMDA0xEmoMFlkudKAhkT1tBIFeM546/BPPUyT1QLFmjOXiy7X5YwwVPc4lha26V+EZl2xce7ixhagNioOcpwWSMnvaX8YL6/pkxvWRwt0EpDq3TK87XVQD+OKq6JStzUit2vk+tzXmyNRIn02Y8SI4+qoev51pynhhrVyBu/GNm9ICenyZiNVd8+BEWhsRHOWJRrbiuwQiEYWQjq1ury2Zhh5Q4UYskMgUeU1DwjZYZud39J1F/H1yQDeCAA557eb2U8D+BgA3wzgUwC8Zf3aT5jZtwD4RAB/bf3aX8xLi/sLZvbVAD4VwOc92e23XVrOjjhhfcpCVnbSrE4Au1uf0pqd1logMQ+nu8ao8X5vaYFq9CnZPa6jQQGr2TmJjfVA1i5DC4ZuVn4s9v7QRZgvGBRsdQn67JwyniPzvRRarmJpvTWTZH6Pesj3EVpdI0fVFWlh7H39JDTqgIIYaU1JL8SoNA/uc3MW3M3WuoxBQd5c0zhB+hKCygv+OTe25VpRNLbNSICnsXGIUUFKeCSKQWAKdYxBYErGD6XZ2ayyeWSHobFhfkaRnZzzT/beCLG+wswGAP8ngD8JIAEYcs4/e+s17wDwhvW/3wDgJ4WvvaqhM7PPAvBZ5f9f97rXXbL/7kuhhakPW3pyO3KHA1nwz9YVD6H8Qzw2AWxufdoZMfIOi7O4X/YQeomwmcsF4utyehJNV7MgOwQ/Dvo1PpC1labEzOgGbbk/qJNx/7UR+haDKEcsorm6/HVgkdSS7yXXJemz/H1HRJTloVD9dSoSRSNG23OodV3tOZRtwIGhsYmH0MX4ILlDoV3wzyI7AwaieZgVWhiAmaTHRehmnBubYlDAW3BDqstrl2zmNVHDyDd9m76JbKrvw6p+ws3sreu/325m3/nKf57MFgEAH5tz/igA/zaAnwfw19c/f+Wd4ZWf5hz82vKCnL845/z68s+LL74obvvJLs2gYPl3a+tTWVvTa6LWmJ6xTQAb00kU0TigaGtUhKt8HzdhbY/sdDrMiMYSbC6QbGk9GHXAB25T+vhGlemjTsIwZKnLNjsRq/e2OTCaVjCiMSJtgRt/LsqXZcSI1pT0+hxr19e/78SGQt5wTHdr5IY3KhKV7IDBsj9sOmnC8YQDRiR4HwtJ8I/FKps5jJdDvmJ8QCFRQr6MQo/bLKIZZGdronjB/8jk1hTEiKKblaaEcHk78IjR9neTsoj7sLwr9KfXf392743UVs75p9Z/n8zsTwP44Zzzz5sZzOxX3kJ3PhTAT63//VMAfjWA21/7P17xtbc/5vvu7VImoSOprYkKWdmJGm/Vyj3EI8JxpW6vw0Evzji7X1ZbE0WivLoqmsG70nXWAgnXgTkwA8vvzkx7T1AOcoL2bq/blsamCfMTnq8JGF5Wl/u8AQUNbDtc2Oo2bs72w7iqBWpMCyOHQqfo/cF1eYs1Uf59Rx0scM2ZWjfbgBvMvrGEMMkvdUdMKxXy/GdpD/9k63LGB3nN2WGRnWSDpNlhLJd3NzbGcpnX1ijIzm5QQOxXMD6wxCMww8C70g1Zc+e7D6v6zs45f9f673/yZLbz6mVmrwVwk3P+V+sf/V4A37P+998C8BkAPm81KPhAAG97xdfeshoU/HYAf+TW1z7dzL4Wi0HBpwD4uN4/S+81OZSe20t1yeIPzexhXBOy0gJ6+ZDP0T5UgwKa9hGdLLqHpE5NX6e63REjUbPjaoFkI4yBNihQDs2A0KAJwxBgcSpkLaL5Q/Pybxox6qDZOc2BpoTN92qMPMjaGhHZaf15k58XxjYPokbOxPuDOGxqHb4MG1cExrm+W24NGSpqw1LXOTenJAj+UTQ7hEFB4vNllrqj1JS0DhXFtl8lBFUR/Cv7JRCjzBsJDFuoKGN8UJCdZ6TZMbO349VUsW3lnN/UfEevXh8A4H81sxEL3ezHAfz+9Wt/AsBbzexHABwBvHl1YgOALwLwZWb2o1j0PZ+Rc/6F9WtvBfDRAH64vDbn/IP9f5S+K0TPIB+2tBZI1Na0risf8lnNTmfESHWPYyeLrXOXVISrNLP++6yPFmhDjERaY8pL5s65pR5CWRMBoBzGhaaERGA8fdOr6o48pa+LFihAC2uds8O6S6ZU8r3a7neWhywaYsTnkXFDoVPYoIC9rzceuqlaQXnIohkUHBsjO8kOGJDW++t5ZCdPIj3ORowCLYyn3XHhqph5y2WYYSYNFba6jbVAJtDNFEOF3SKaQMAFJMqyUPeeLO/KF/rax2Oxaf6y9f/fAuD7Ou3pZSvn/OMAftOZr/00gN955mvvxoLYPO5rMxbU58GsImTV4fi2D4M9t4ZN2u5kfSprjLhQUfaQr7oB6W5WZPPQHJETDzPk5FanNXKHRdXS+jZNsPY7USe3rBao/N1KU8LWXnJg+CaKrpsynjtozRmdw9XBIlqxcqYP49EQX49uJn6O9ZDOPkwANSyatsoWh1g83bctjXgWry+GEQfMbjM5i03JYnxAIDtF8M80DyiIkR+CutflmygmBFXS1mDNBSKsspEEepzSPCTBRa/UpcI/BSOB0kQRdTc77WeIxvZPAMDMPg/Av5/zcrIws68H8E3dd3dd9NqErK1dvYK0sF70ATakk3dx4mgfvWhhd41uxrsXtaV9qNol9f3bWgukHuo062keoVVqKy5kpS7l8pYSXku6OAGCFiiQ79U6v0e/n7W9T4YRWrduNHyZNTBRh01taWG8xkhDjNhhk6pdWlzTMq3ZUZqdwZL/OVZpbCsSNaWMR7XfyWYkwDc7lGZnpVlRBgUQDBU2+haPwHB1BWRnEJAdgca2a4EIJCo/PBobO457PYDnb/3/c+ufXdcdWSp9gJ6My4dmjvYRFrLSglOxbmOOu1pXNhJojBjJuUCiEQadz9GLJihrE9oe6soBP1MhvryAHuCbHVkLRGp2FoOC9logxXBl1wLVf2855zW4UUQ8G2treFevvpq+1kYN6nCBfV6E3RobN306ssMaFCyuaV5dKUxzrctYWudJa0oyViTKq6s2OxgwUlbOK93shnSPI62ydwSmrRZoR0qEukTTN3Ta75iF5uyeLPYn+SoA325mX7X+/ycD+Mo+W7quyIpMmgFG8yAe8kmNhmx9Suf39MnZUWkqMhLVvK42sdzqshouVRPVOHeJPoQGAjqBDtbpW7Ne1wIttXWDAgbROKVE0wSBVbND5tZIltakFkjRILJaoCgyyVucdzrkq1qgp0QL059DL/++cyuqreGbvtZIlFYXNmDETIeK0k3JLQSmXlelmw1U3Zx0ehyl2VkP4yNtlc25vJW6FFKyGRQwCEyEbkY0O0pTIoSgbnXJpvo+LDZU9HPN7DsA/A4sJgGfm3P+u113dl3S0mlhLH1LDzhU6spIFG2p2sc9ThcKkxaljWkUKsJFH/JFjruajyT/3kirbFkL1AkxYsT8i4Cebx54zU7GCzdK3YEKHY5YWndBjJhmp5Pgf46+fxtra2SDArouR+9UNXK8hXwn7VInxGgSr28eVGSHb3ZGJLzkfd6S3kQx+7VZsHLGbqjguWFu+T0k8pBsxEhQck2puxofMEYNkkGBEiqaBdrd+hoG4RqUuvdkuT/J6oL2vTnnfxPA1/ff0nVFlkoLY/MYZNcp0UhANSjgBbIaF53luLfmzvfWLulNqieYjiFG/AS7bfMbtThnDjNKFo4ioJ9T1hCYXjQ2oSmRLK0JLZCa7wVwWiC1+WU/x+Vz0fz+EDQaaU3v5K2c1Sbq5fs5t+T7uqpdan1/kGlsIw6WMDvJw6prWrYRB6GJYpGdbCNG8+vugn+WHjds+63+TpLoSke6vEkIzFqXcmNTEKOtwSCaEsVIQLC0HvO0wBrPkmZndS57p5m98AT2c13BpecmFBqFVzfIaSa1QCoS5deNuiK1pYWxmp14CF/jwwwZKioLm0VaGH9Y1EJme7hOKQ0J20wCy3tYQUpoGptguQwI2hrvYBKoqwrz2bpy8yu6NcqH5sY0TBZRVg1Melk5sxpE9XnBajzVfC82jFv9vdmGcNUPzlmkm4GkmxWkhEd2lvwe3/ig0M1044PaklzIsDRnjBubKdoaADMZrmoB44ORoZuhj6X1oDRn92SxGNUPA/hWM/tqAO8qf5hz/gtddnVd8tLdapZ/0+467CGfzVUJ5jzQhwNRC9SaFnZ3wv3EQ36n60vnIzXXcPWhF51mPltGqQsse37NI97dbEFg/AdYBNl5z8TsV3SPI7RA6u9tq0sOF1j67DAYBhNomI2t06Ohog5AcGu40PY+KbvSqYgR+znuxQSgkSjNWCLbchQrmpyzr0tCDgx2epzXTMoIjB2osNLd5Y3X7BxwdJtUiRaGxY2NaR5MFOazIaiStkZwTRsC9DiqbmkMjb//3vXFNjvvDeCfAfh1t/7Mfwpe1xNbqpDVzBbaB2tQ0FizI+cxqPSMThqj9uF+fSa3qgX30xb0bjRBsa5vaZ3W11NlJXqRauMMkM3OrNK3BtJ6WtQCjf79Qc33AjgtkHrIL6/lhwDafmnEqDXiqR7yRWSnPX0r5h7nfo63ZrItAq7nI63NpDe8Ud9n60F0nuoUoyL4NwHZGTETRgLLAZgK6URBdmYcvW5n1pAS1lBhC72kLa0HSgs0iIhRwsghJUnYb9ECUU2Jbj3taYFyzhjzhAkHHGq6qXu2WIOCT+u9keu6bEUOB0yqezzfgNN+tNYCqRx3OlRUpoX14rj3mVjyTWofWuOkIjt0k7o0DlWx62PqModbBXXYJ+4cUqKiRnyYZlstUPmy2vi1zkfi62r0LWDRN/L5Xq01OzH6VvO6NAKjHfK3/ZJMgNYum2GDAtadj/0cW7k/OHoK1SJ6YI0ESl2yKRk4ZEdGSlbjAx+JKrQwDgHf6ua6G+a+X7YuZ1CwNSWSFohpouZFjKK4vDl1p5QXu3I70GjIfVjUz2JmBwCfCeA/xILo/AMAfzZnQul0XU9kqVa4ACvo7UOzUh8GOmLUlm4WRYxaC1npiWUvjruIyPFJ8X0mzapTGO8OFaOxUaGXs25Q0DpME1gOuDTiKdY9OTwrVUtR6rb+HJc98BojjWbVC4FhEU/1c+wjMJ0F/6KhDTvEYptU3ppepGEOhcbmHJxFuhnsgNGya3yw11WQHUKzM+vamtF8ZGfPl9E1RmOlkZEE/1gMFQbGSCCLzRk4jdEIoTnb3Njq77F5bXaS8VTq+7DYxu2LAXw4gL+0/v8fAvBGAP9Fj01dl772iaV2SGKT7XvRElTNTvNDPll3b86erqU13ZyJhxlWszOr+yW1NTJiJGh2tMahfJ8vSJeyZUTNThdkR6SxMVog9RBa6npaIHVoASyfZfZz3NzSWqRvsVqgXWPUKS+rw+ft9j7cuk9bu6QiRuJ9nf4cr4fV7Gl2RCtnDCRilDTkARtS4tHNRKH72pR4n2NTmxIbMeLkIlF7U8LS41jNjk67Y2hsY54wY9yGJ/VNcKGipznhxmYke0i4Dt/s/HsA/q2cl9+qmX09gO/utanr0pcalgeA0uyoQtYS7tdc6K4iRq21QEFr5NZC1qeezyE2JUoI3zjwdDNesyM6hSnITjfNTiBnx7kOKWWkrDUlB0LwP4vDhVLXN6zQEaPDMFC0RoA/5Jc9tLaQX15LIEa9tEBBK30aiWo+FOoTraDmI6l0apouaRyyUw6fVXsdAAAgAElEQVT5bLOzGx94zU5BSrhpfsnZ4V3TWFrYQo87eXXFHJiFxvae9fdyfi867Y6ztB7zvFo581bZXgjqnPJCUbRD5Se6tcgQ1GnOS8DtA2t22CefveK1tv5zXXdkyRxhLAe71sjDUrf9JJR+2IoTSzX0Ug/p5OhbMsed1i415vrLTSrvSqeK0Zm6C21Bt4imNDsh62kO2VER2pzr6GRE08fcH1Tr9KUuc3+IIUbs4VZpVBlt4zZsEvbLaIGitDC66WvsbhbVLrWmhbEauZPYTHZDdsbluMo2JQPpblaajOQYH6hICYZxORDTzY5mqODVldzNsNDNGI3RmHVDBQaJ6oEYneaEG0w83Ww1PvCasyll3GBGfkZpbN8I4BvN7K9g0ey8BcDf67Wp69JXyKp1IOg6kUMSw51XhayiRalsudyYxtZbu9Q6tHU7dLg5OzHEyM+7EO2L2WZSdDZTJuPd3Ngu0AM9OvN9qnEHsAh5W1unL3UZ17SgFsgdLmi0sLIH+pDfGNmJhn+yn2P+PsnmcMWQKP55wdYFV7ezxoh+Hhvb7GiHfLZuJLfmQISKqjS20jz4TZTgQobbWqD6/cHUpgTj5nZ37t4LLE1UwoCB/BwzIahFW6MgMAmDq9mZUlo1Ow8L2WF/mj8O4NMBfBIWROd/A/A/99rUdelLniRhfdg6Zy+VFrbUJbRA0TBNWguk5QKxIZ2ydqnxQ1G2tFY57q5wPGGwHbny65Jc/znLFCumrpvE/YqluFk9OghZOFuz7iSk57wEljZupNT371KXQHZCiLJ/fwgZFAyGl3pogQZfCxQKQWUQI9XqvWiBOuWc9cq1Yi2i2SaVDUlW85HYoZA6JLRiUOCEikIMvdyQHaeubTQ2re7s0O6kHBjcco9zrq+M7NiIA1F3zJNENysW3LX3WUqFFjbSVKpMIDvTnDFakowEZvOtsguN7Zk0KFi1Ol+6/nNdd3CdVI4wNAGyJMYmOPnqQ4bWAgUFsuwEUJ2Etp5Y6tqlthPWpXnQDqDl+9y6wnuM1ewsiFH7/apZOHtTXX+dOslfXusfwGLDECL8U8z3Wl5r2/vz3Io0JYexj3sch+zoyLqEGInucawrHU8L0+hb9H2HDf8s79/GdTcmQGvLcJFhYOvn3TMokGlhG43NqRugbzF1I00Jg+yMPesaBCOBxfig9n44pYQbzEicsmar67m8TanQ2BRkZ3Stsjca2/AcXfc+LOpOZ2a/ysy+3szevf7zdWb2Qb03d138mkVaGKAaFIiHpNacZrCT0E60BPEwswtkueurW5+yTWpjxEhsSmgNgUgLUyzDI1og5vemfibK91XrBoTuTNDhtB2ahcZv5LVAdwUx4g/jiraG1wIp72FGCxQJQR0G3kiAfQ8PbPMQ1Ao+rVDR6H2ScQU9DLzhymY9PXEGBewh30gEZqOF0cjO8rrJqys2JSihos65RKbHDVzdIc9IMNmooXbvnZMu+E9YEKNcG2ClxaBAaXYykd8zzQ+TxsbeQf8SgG8D8MHrP9+GK43tTi3Z/QVsqKg+sRzM3ENdZGKp5Gi0Ft7qOTtPG9nJsADdjAnLCzUPBNe/BwIzB/UvrQ0KWJe3iOCfaaQibo3MZyMyDGG0QCGDAkoLpDeTXB5ZDDFihgtAQAvk0JOjltbe51ilH9KhotvnonWos4hE0Qi4NgzZaGyOnmKjm9FGApwb247AaIiRh0SpuTUYxuWg7dLNtOZsobFxdDMFgSn5PbXPxWnOq5VzoG4NMZqTbCQwU4hRxsFmZLZBvSeL/Wk+JOf8Cbf+/wvN7Ht7bOi6YivCcT8MhmMPOslg7sM2wnGntEBBq1Y+n+NuaHb8ukkOpgQIrn/SncKWur6RQOiAT1gYv/gcf9NWNDuS7sP276utCEJLaXYCrooMyhXL9xroz7GsBWIRIxGJOjpuVlGNEYtU6wi4c18PaIGM0ALNMt2XRHY6W3DrFv3+gVH5TOzNg2NQIB7yt7pOzo6pWiAjNTvQBP+LkUB2aa67tobPBRocq+xC35rtQB+Msx0wOMYHEaSkWHAvz5nHv2YxKJiQjLwGWF3pPIOCeTE+yM8osjOY2QeW/zGz98fVevpOrX4C2cDDdhS0QCKtxj3cihPLTQtEWkTL4X7sBLu1FkhENBQtUNQpzKsbcmNjEKOQ2N+H+rXPxCqYZoXYkT1XKQ+6CxlXNza0aO2SVV7Lf97aamvmICJHI1GqFsi9T/ZBuFQmwLDdH/xhyFJXRKoJZEfJ99rr1l+n5nvZ2mR4zc4gGgmwxgeqtsY24wNvv1rODm18sDVRJN1sWAwKavffQjdTEZgD5qoWc68r0M1KuKqDGI1ISAICk2yAEVqgEc8usvNFAL7HzP4OFuvp3wPgT3bb1XXJKyZkZcL99KbkMAx8qKiKGJGT8da5H7JAln7Yas0krQUSH7YDu98oskMc6h4d2tct3Hl2scnrJ/E68CGzscM4UEe5Qk3J6KN9oXyvW1qgczRLVUsBcFqgiJW+QvdVfm+MFkg95O91ufuOpF2y9tol3kpfs05XLKKVz8Sey+YfGJX3mMkIDI9oAATdTKSx5bJfB/EcRMH/bpV9fr85Z4x5woxxo9oydb0Q1FNKK91MaUoWxOilyvvhtCJGWlMyYsBL1f3OKeMFTBICsxgqLFqgcw1+QbhOz2Kzk3N+q5l9N4DfgeWt+yU55x/ourPrklZkAqgJZJUHQnv6wFLXR2B65QLp1qfc4VYVsvLITgodxhn3LdXenKl7SgkvsNM/aC56ES2Q9z5T3eNUI4zWGT6RYchA2GWHENpbWqCzuUARAxNjDCBiVvqt3Q9LXfaQ39pFL6pdcqMKwrSwtkiU0uxIeqgyAPCGIbOmQdyQElfwr9HYbBTrkk2JGYns5FmjmxHXYV6F+bMdaHVNtgGDo4Ep9C3FSGAPFT3/mjlAC9s0Rs49/b1Ebc2237xTqx9X94CZp0rek0X9NGb2BgA/lnP+v9f/f8HMPiTn/C+67u666NWNlhAK4Ru2w9XZukGNEe3iJB5m+JBO8mFbrJEbC1l5jVEnYX40TLNxzg6rBZqDWqDadShZOJpmh21+Yy5kXu0oHer29z5uRS2tgbpOI9I8jMPAW3s3ztkJDVkELZCKptJIVOOogr1uayMBLd9Los8GGnUG4ZJobOvBMjemm7Ganb0uSzcrxgf1/Y5Qc3ZK3fPIzoI8TBrdbDjgxmYniyytTYlCY1vocbUhy2kL6XzE18WwhqDW0foDEmYFMUJBuBLGM7/rElaa2Qb1niz2U/415J9d11NaIToJIfgP08LcyWLgMDPyE0tdY0RO3Gm62RLuxyBRPehQJ3ViWRAjxqBAaVBH8tARREooS+vGdUON+sgfkpbagQbY4XfrdX1U7hKNkfcgX+qqCEx7a29OYxTU1pAIuKyJIo0EVNSTCfEte2BrAsxQKObWyBgf9NAKnkQNYkFgwGpgWASGbUpEGtuORNX3O+YZCcPOv/bqWtE2nt/vlHQEZjNUqNVdERjJcnm1tK4PbkrzICAwpW51gJVwEGlsWwhq5TZZ9vvQkB327vEo5/ye8j85518G8LASh+75irmmDa7rSQgxYowEQsiOrwWKcNzZcD9FyLrU5fJEWlOWlq9rE8tdC0SkK4c47sSEtfEBf6kbQ4w8ISsQa0gY+gsQQ2Cqmp1ImObo1w3lezFaoIil9WhI2csFih3yaeS3sRYool2iNEZBJKp1PhKPePZBUqNIdevw5YFEdmQaG+nyNog0NsY9bs+X0RAYwEF25oTRtHwZlCaK2K+irSl0M8+ef7QUoLE5GqPSlLAOethpbFUkappxsPTgDArYT2NeHdgAAGb2Abi6sd2p1dv6VD0w0pqd1kYCgcPMIrxlJovaW34YyIdiB0RDnVjyuUBJbiQBQgskI1z+fnPOMmLEIGe9snCA2NCCyfDZEE9RkO7VDeV7EVqgyNBioxg5U1a5LoEYRSytlfBl3fq/k8aol/U/lcOlNb4AZ/0fQowaI9WbG5vT7Iyya1qp6yEwwbqV5qHkwCi5NRtiVAlXjdDYys81V2ijkdwakFqgG0xa87AaH3j39AO0piRZQYwqr5k1x7/7stif5s8AeJuZffn6/78fwJ/qs6XriqwYx71PCF+vcL/RzNcCddMu6c0Oh+yID1tSCxRFjJgk85C2ppfGyEmuBvRG/fb3PrZuAH1hEa6tkZKuxfLvOm+8IARtG7+olTNQb0p2OmpMY3Rz5rwSpYW5WqBOw6YYijjgdHKyNAI0QW6IpRmumK10XyIvK6KtoYZCHZCd05zw2uf0Q76PwIg0tlGhmxkG0SK6Rjcr2g+JbkZYWkfpZkD9Okwp45GK7AwHN/yz0O6OYlNyQF1jtBgJTFJTslhl15GdeRKDa+/JYt3Y/qqZ/QQWy2kA+EM552/tt63rUlfk0MFodqJCVr6utt/3TP7EUhGyAiv9pbGQFeAPM6pFNKMFWqycA+5mhGYnYl/MJKR303201uwEGhJauxQ6NK8UjWrzEENovboh1zQCiSrDm8jvrn5AiB3yWRfIHvRZIKIF4rRL6oCB+Rwr9zNgea9TQ5YIzZWhsXWoq2qMdmSHdTdTkZ3zTUlKGcOKwLA73rVATlNiWm5NqTs7iNEBGo0tE3WnOeM1NiMLIZ0gQlBLqOhLiuCfCUGdC7IjhIraiMFSddCU5uPyH88osoOc8zcD+GYz+zeKK9t13Z0VCuETBLKqGNvTAoWsTwktkMqVXvYwNBfQL3W5w0wvxOi9O2iBTiKNjXUvCtNJGgdeMoeZixoS8vMWoQrWPhsRgwKubmC/RAMcEfwrrnTqe6Jogc7mAkXClwUam6STG8x3P4w07OTwRtEXARzdt5eByTSLuWGKxkgauK1HMcaNzUAbCQxEE3VKC31rtgN9ICyIUa1ucTfTtDWcFuiASTrkF+ODWt3NjU2IQGARI7VuHkY3VHSaJwyWRWRn0QJVmQsr1c8ELdB9WNpdaVlvbb6L67p4xUL49nA/r+7TFrIudTlLVbV5YCxVJ5EWBvACZLU5e5paoDlliVrEuBfNKSNn0IGtAEmxCk3FfS1Q+Z2GGhL6cNuWehfRfjBBs6F8L0YLFEQe/LoR9zjfla4nLWyp215boxquMC6bpzlJn2OAG97ELaKZ+6+ubWxt0c9aRI8oBgXkwbmgKm5ujdaUMG5s5ZCv0MKYvKHdIlqnsc2VukXwrxoJLHX960BnDQFAMSioDC3SVBAYDdkZMTt1i2bnYdHYIs2Odie7rieytsNBaypQRAtkyqGurRZIpSWwdVVaAsAhOxHjA+pwoIZpEuJuYJ2EBowPqMDL1gfbUKPuI1GnwBSfdqULGncs31u7FrFJ/u3vfdyKauSA9pbWVC7QJRoj4vrK4Z8dkHVFW6MsFolS75ODMe6SMbpv8yZq5D7Hs6rF3BCY85+1nDPGPGHGCJBN6nBY6+b6If8mSDfLuX6fVJuHjc7n5OyoVs4Y/LpzqK6PRBUamxb+ObiW1ru2JhIqer5uuUb2wDQ7kWbnW5rv4rouXr047rGJJXMYz7gZ9clia6vhrS6DGIkTS66ujhjRCFfkcNCYkz8SlKWIGH074FftlnU0Q9ECSYiRrNkJfI4JbU1zquAFIaiUCUSAKsjcz9ojRjFkvUcuEDcU0uhbAK9dkoc3o08jjtB9GQMemR4nGI1EQkWR/EO+IvhnjA/KIV+imxUaG9E8RIwEahqjXQvUGDFamxJJq8IgO/OM0fL+O2bWanxQe/8WBEar61talybqmaSxme3Rrznnz1z/7H16beq69BUNtQO4Q5I8sXQFp0mqCXBaoNjDtg9ixCM7nRCjxtqllDJSFg+gAmUpMsWva3YKOtDWWvaShoSxrAWCn2PKma6tZqdX3VOgLuVKNwfeE4ROYwrtd9ECZec9PJhmuHIYCc3OHLj/klqgHkMhVdO31e1kfMCEW0vxByVUtILARNzNhsHXApVDvla3NCV1+3i12dmMDyr7LQYFqpWzV3extJ4lLRAIo4Z0WpoHqa6NGCxXaXcbjU1AYLL5YaV4xpGdbzezDyv/Y2a/DcDb+2zpuiLrIlckItxPzThwtUCzLmTlEaP2rmnxul4+RyS/h0F29OZsGBz61gUUq7re4RLqVs0FJ9aoL3Xb7pfW7Gzau7Zo1ClIhwI4dzPpYCdoa1StoFc3ZFBAUJdiBgWMK10MqaboqBFEmaor7pdBoiL7JepGLfq9fC/1vj4QBgWFFqbQzQYiv+dU6FsSjW09CFO0MF3wzxkUKEhUobEdz75kSmnRRAVobLVcoHn9O4upA7NyMbSpNVHr1xRkZw8VraHqD7PZYa/S/wDgH5vZ5wD4UAD/GYA3d9vVdcnrEo67d2BUchNeXjfj0Zkb/pR0ISunBdInluzD9oWbHjSKiFVre3rGUndAbXC7O+jpqEP7rBaGstSZ2ikG1y51fSOM2/tgVi+DgnIYr5s1xBs/JmentbYmElZK1Q3QfW9/Ns65xPdygQwh6+SwSb7/UghM4L5OIEYyArNuoVY3gnYyzc6cAvStcsCuGB/MBYFRjARGBtlJeNE0rcpAuLwVxEhpSjYaW22/U8LBkqSB2ermSlMSEfxvVtk+siM1JcNiUFD7HOcHSmNjc3a+xszeAeDbAfwMgI/KOf9cz41dl7ailqq3v/dxK/TwIqlLOi2Bce3po62J5ewMOFYSm4HlQRNBjGr7zTnLgt5Sty7M1+lbw2AwRwsUcuZ7ivbFIcRI1OxEDs2cW1jbpiSC7HAmEHG9VR3ZiecNMRoj5VbJ3X8DCK2xBgUdLKLnhOfPJbqeWVR+TxDhYjRRymfCzNwhVsQYZT/k+25hSrMzrAfhOrJTEKPn+bpbfg9huazk1hC5QAuyo+XL7Nql80hUmnW6WUGtcuU5X5ASKaSz0O6m8/vNgboF2ak2Ow8U2WE1O28C8DcB/LcA/imAv2Fm79dzY9elrahAFvAOMyl0GPfqFoMCZbFaIJUex2l2OuXshCas7MM2oAWqCv71KT7gI2dbox7J7+mgN7u9p8fXjaOovGYnQoeqB9vdfi1X19cCXUTpq2qBOrnSBQX/S93K9V1DcSXDFUILdAoc8g+jrXbuDo04YhFNhA6rz4tDB20NsLzXnV6nC2IUGbKUpsQqCMGeW6PQ2Hwt0DTrdDMmZ2cO5NYMhAZmac4mkW5W6laaqNOKlIQQI1+zoxoULHUryM56jQYFgVlDUGvPitL0DQ+s2WGv0lcBeHPO+W0AYGZ/DMB3Aviw6ndd1xNbISErKUCOGAkA/iFJfsjc0gKdDfdLGc8d9IfivbJqdRCYCI1ir8tQi/ogRqFGvXpQjGSq8DQ2rXHwKWHL12MWxgCJcjXWAkXzvQCOFtZcYxS5DoxmJyKgH/0GOGJNvwXY5oX6+7gVPeSz+T3KGhjaXQhZrzeom+FKY43RvKGzOrJTNxJYBfQBg4Kqu1lKeK1pCAxT97Tu96UIAlPb75TwyGI0tlpzlgJIiRFNVASBgRXNznmNUQiBGXz3uEJ5HA7PZrPzppzzz5b/yTn/OTP7p532dF2BFaOFcXSziJAV8LVAkQngUreiBQoJWet7XerGEC5PyNoDMYrQwsrr69kn+sRyr3v+6xc5elFi9LahoiFDBdayNkBj4wT/fWhhsXwvIqQzgCIyqNx8QTPp03L1zxvgIVH6fee2FnM8M1Wf5oxHh/YukAXhUuseJ99lU9HeLXXrGsTdtCNwn6w21AHdXTmwVtHDNfxTaR4OjBtbBNnh6i6Cf71uroagLofxHGhKasYHqVDRIi5vNUOFQFNihPHBZlBweHT2Na8uvDY7Fdrdrtl5WM1O9W5nZq8FgNuNTlk55++5/ZrrerrrFNRoAP7BTm6iGM1OZGJJ1Y1MLDsKbxsLWZe6A2eN3NiVbg4cmPe6vh1wey1QYMK6XrI6JUyvu2mMXMH0BYdxxvEuIvgnmhItaJZHjFojUcUlS8v34oJxo/ff1ppJJsB2CjwvGBfI6H4ZGrGOGNU/xxETDGD5LFODBaFuOYxXaWxbSCd/CB0Jg4JISOduqODT7hQNTLG0rhoqBGhhhXZXo4XtrmkBo4ZKs7MjMIp7XNFa+U3JoLixEQhXXrOeSiDtQ1nep/FbzexzzeyNt//QzB6Z2e82s68D8Cn9tndd7JoDQtZuBgXMdDzimkYdviL5MhzdLDKxpChAgf22PihudavZJ7FJqCdAjnDcgaX5be/oxSA7EcRIRHYa07ciBgWMkcBFIZ1MIGzr6xBpSghtTSTXitICBQxXWMRIef+WupwWKKYxOreK4Yp+nxzq97PA0AJY7zsE8ivVLb8LJwdGdU3bDsJVzU6gKSEMFQrtTnEhM8Iqe8uXCVyHGmKUL3BNq2l2ckQDYyUWpIJEpeO6BR0xYmhs4yggRvdgee+WfxfAHwPw983svQD8PwBeAPABAL4JwJ/KOX9n3y1eF7NCQlZK9xA45BMc99Oc8V7P6w9FwNMCBWhs4xLuV9cCBTnu1KG5dV19ig/4k9vIARTwkbP5giaKoZO0Dv88BdAM5iB+++utBf+XWFrX6+qNKoPARPRhlJV+QFtD3c8iSPWm2Tn/mlNkiEVogU4BWtj22XC0QK0/x5fcJ6uhw4HPRHk9Z4wivM+2psRpfE3Lw2EsrUsThYDLWxXZmWcMliUaG4MYzVtTooeVooKclQZAQWAGQmOUArSwjcZWac6w0dgU9zii6SvN2bOk2ck5/zKALwLwRWb2egCvB/BLAH4o5/zSE9jfdZFrCoRIciF8EWEow52P0cIAbxIam1gCC2I04NV7CgtZh7r1acTVa6/rP2xbu91FAmaZuhFL6/J6ynmrkxZIeT/QoaJBSt/tfT1uRbRAvRGj2kH0NOuGK5swv9I8RK3pgfaIEdOczSnj+XMhPG7dtvdfVguk3n89wX/kc1zqnggaW2stZgjZWRsYz43tBjNmAXkY14O7Oc3OjUpjG3xDhQ2BCRzyq81DRAOz0c1qzUMkt8bXAuVAEwXC2hubG5twHQjNTqk7PjDNDn31c87vBPDOjnu5rgtWxLWHDRVVhawM7SMiZGU0O6GJ5a26j4uIiKTaAz49LmoRzVqfRuiHp1OdlgC01wJdVrd2mNEzVSSxf6ghqQuxIyYQWkin0vgRRgKXuMe5mpK4MP/cUkMkX17XobEFEM/yvedWhD7LaHYikQLeZ6MYrkSGN1UEZgsH1u8PL031ZwUQQ5Sb33+H0uz4BgUnSauyHlgdGtsohnRuU//KfucALYxBojYzgNZNVASBIeh8O42Np4XtyE5NW6PT2EBYZRdUTTI+uAdLu3tc151dERobNRFO/YS30cOBz51vu9/9QNfnkN88Z2fWD/l7XV+fEaE11jU7nWhsnRCjjR4Xcgqrvy7SlLDhlEvd1hqYSzRG9feaOlyg72cdBP+nCLJO5ezEcri8upH7pEd7jgRbl9dPlQ9GxPFvq1u7Bp3qRoKBt0ajcgg9rdoahRZWtB91ulnCwRIQoG/V6m5WzoGcnWrdKZCHQwj+ixubkltjBAITEvwPDBI1ry8NNDu1uut+DzcPC9m5NjsPZPV4eAHLAyFMC3MeCPJ+He58EbJGOfnnHmBRIethGDYt0OPrxmlsFLUocFisi8aDHHdP0BtGdupaoEuaKMZFLyTK95CdkOXyOgQgxNihkE6nUTWZbsZpjHoc8nvcd8rXIuGfPeoymp2IVbaHREUNVxZk5/zXTxuy0/ZzfLoAMaJCRZX9rk1JDdmZN7qZfrit1l2bB82gwDc+SKcAjY1BSgoCIyAaDO0OK1Ki1DXK+KDQ2BRkh2ii1mZyDBgUlAbssa8ptDvlfXYP1rXZeSArJGSlJqEBWhhlUNB+v5EDHVP3kpBO4DwNKJIDs7ze0wLFJpa0y1uEblalqcQ1Rj24/n6OkX6oY1zegJiDXNkGY7Mba9AcmlUw36suHtfdtxhziYVm1V6DeAo1D33uk0zTdwpECnh1oy6Q3v0sopHb67ZH1heNka/F1GhsxXqaya3RNSXVpmTSrZEVuplCCysoRa7tN1SXCRWNNCWKlXOkKTlf17a6At2MoMdtJg6KxugeLPqnMbMPAvDG29+Tc/6WHpu6Ln1Ncz8hay/hbdyqtTGNwrGA3UXjcYTr8VqgGLJzGMx1cAKCE0umKQkcZl6qhAZGdB/l9T00UZ4rXcQ9rryUtZ7WaGx+I7WjiI1zdkL3By64tQ+yc0nd+nu4h0X0JffJc81vShk5x+hbZU+PW5EgWMC/n0U0coCgBYrQ2GpaoBCNrSAwdW3NI1NpbGtdyt1Mbx4YgwKFbjYQuUBbbo1kaV1obIy2Rt9vTQNTmpJRyu9ZXjvXEKPNIjpAu6vxqUsjpISr3oNF/TRm9t8A+BwAPw6gXP0M4E2d9nVd4poDwlDOxUmfAHoc9yJkbY7shA/5dRenS4SsgE/7iBgUMJPQXlogue5oePfRR6Ii14FqdkLITi0EVd+vmbn7BWIHRtagwKwDshO0ZPfr6ggMZS6RMp4TDVcYzc6cdISL0gJFkHWnmbyEFgYQyHrgOtQ/b7Emind5uwP3X/ObndKU5IBGo4rszIVuFrFybuuath3cK9c3Yo3MNGexuut+q1bOqxZIQGCM0C5hvgTZqdDYciBv6B4s9t39BwF8RM7553pu5rriq5eQ9ZJQO/eQ3zgXaI7SljbNzuNvsFEhq0v7CNOshmou0EVaICqELxD+SYSKhqyyGaOGQPNLTZoba62AmB0uE9IZoVltnwunbpQW5uX3dAnTTAmvHUUEnMwNiwwBvLohJMqpG6WFsQYFkftvyssQzOzV3xul+3phpZeEL1c/a6FQ0UJjqw2FipZCF7rX6qaIRmPwEaOCdij0ONussn3jA8mggGgetqZECdNkLKJTaXYCNLZKE7U1xoHrUEei1roPzHqavXv8y/DUxyUAACAASURBVGujc7dXxKqVzcORD4tjXcgacZxa6nLIzt1BjLxJaGy/rBaoNcf9EreleghfnMbWT7PjW1q31hgBO4oYCv90Dvny59ihQy1fiyO0HsUoihj5wvz2FtE9XCBLvldrjVHUpdB7n4XvZw7CtQ1ZAnW5YUjb+9kcQbjW1w4VpCSX3BqlKSEQoxQI6dzrVhCYSc+tGQnjgwgCU+rWaGylKRkF17SSN1XTGBUERsmtKU1fqu03QDcrTRQqmp1Cu5PokvdgsVfpG83sfwLwFQDeU/4w5/wDXXZ1XfKKhIoeiId4xPjAm7BGH4oedz5MSyC1QNHDzFnEqMGE9bFaoAssrZlMlYgrnUfVWepGcjR8q+zmltZRYwnn8AXEDmD80CJ4GHca1R4C+ohBAacF0um+lOD/IuvpM/ezizWIbbU1nqPgpYj9MrB79dcvuU8yhitjxHDFGSwA4vUlmpKpNCVSOGVBYHx6nJbXwhgf6IJ/xvigHNQl1zSCxoYA7Q4EjW0L/wwgOzUkamtKAnlDtSZqC7Z9Rmlsn7b++5Nu/VkG8GFtt3Nd0XUJslM7jOcc40qX7z9XF9AfXt4E8NKmpDUCs0+aH//1Syes3n6bW1pfoK2p5110MigI09jIHKMAMuk2O2mhQz2OznNuMZqd0xwR0DPGB30Q5SllvPBIRTyx1q07ZanvM0Zb0wNZDx2aARfhijj+3a57TtscMe5Y6q6ayTPv30sQe8YiOqJd4vK99JwdBoHRaGxrXTQ+5BNNFCI0tq2J8g0KlHyZkQhX3ZGdtkhU+Vli7nH+fpWmpPwuqvS4B0pjo96FOec39t7IdV22IiF89MO2Od0hzpUG2jclrsYoiBi5SFTwMONNmqOHmYLAnOPOb65pjbVA3TQ7netGfm8MshNufivX+JK8Frdu9P7g5OxEker2iFH9/lDyvSJhu7W6+2G8cW7YBVqVpe7j72dRg4Ly62itbaSHFp3ovtJ+1+ZhqNHCZj23Zndj82lhSlNC0ePW/WqI0Zo3VBNNJj1fhjEo2LU1gpEAQY/baWFK07e6x1U1O6XZEehmhMZoyAFa4z1YivX0bwHwH2BBdP5hzvm7uu3quuR1iaVq80O++xAPTiwdLVDY3Yw8dPQK4QtrdhofZm7XfdzvZufO98nZae1K183S+iKEqx4qekkOjIvANKZDASvNNWpg4lpa90GM5PcDeX/Q87Kc+0PQGIW3iG7rdhdH1osbZp+h2zkjl0vu69Ww0sjQjUBg5klHCDYEBrVQ0TiyU0U0AvQthh5XGgClKRkI44NISOdOu6vRzUpuTaA5qyBcobqDjxgN5WsPrNmh7nZm9ocBfC2ADwLwqwB8rZn9Jz03dl386iVkjU7G/YdtTKNBu5s11hDEhaxw6kYRGE+7FDvMeIfQsNsSOWHVm/W6Fij6PmMtreXPBaXZCRzGHS0FgFAODGMkEDIwofYbcDejmp0OYZpBAb2rrYlaRLt1+9wn43Tfl+/rlesSi2iurnp9fV3YUlfYr/kGBYggMJsbW80t7JK6hOVyAImquqYlXfA/EE1UaR4UV7ph4BGjiGtaHTEK1N1obJUmKut178Nif5r/HMBvzjn/LACY2X8P4B8C+Mu9NnZd/LqEVnP7+19VN3wI7Su89RGjqBaorfDWm1jOF3PyH//1S4T5y77aa5cYjrueq+JP8YF+9JcIQlBDM4DlPRwxgACACitsoW81PuSXun20QPp14FzpdLqZ50p3CYK41O1zyD+LlIRpYR4CE6X7evfJy+5n55r1HTEKWNNTSLWwXzMkDI5FdGlKBGtk8+lxxUhAslxm3NgKjS2CRFXqFlpYKA+HoZspoaJlD1RTIuz34CNGQ8BIYGuiKs3vEECM7sOiP+Wl0bn13/Wn9nU9sRV92Hq0hHACvdeUzMGJpdecXaDRAM4Lb/tNQnshO52a33DT9/+z93Yh2y3vfdBvZt33u3eahKIVa0JaU2tjW6rU74+0oiCICkpQMUqhYg2NkSJEqmiFQEtALPRExZpaiehBbKk9keqJQYnxoCiVFCrWRJK2WkxBSUztfp+1ZsaDWTPra2bWfV/Xb959P+9/DWzevZ9nP9eevZ51rzXX9fsyWQtUWvIQVNsJMXoU2Xn+UNfSqaTaz3/e4p9spOQRzY7EhSwbCZxqgbhIdTJceZWm5GEa8bP374lmZ3FVfA1k3Z4gUZpQ0VZd6X4HezYAkA2FHAbYBo3NZ6REYFDQaB4gaB4eMVSQaGCWuq0wzeeNBDJK0URgEn3rGYOCR1zTkuD/iSbqAcRoaUqeqDt/5ltaIPuZIjuPPu1+2hjzw8aYbzfGfJsx5ocA/EzPjV3r8SXVUjx6uO0lvJWExDXrCi2XkxaITQvr57Z0lmMkm1jm5rdyCNVOWE9zjMjucdnSWqIxeqDZYWuBABmNzRgT0agzwT/Zkh2QGR8sSNQJYtTJwOTpQ/5p8yCkNfbSNubnQ3sYwh+OCQ1XHtZMvoZ29GYtQngAiXryPvPGtnN2FK5pveq2mpIgac5mOl8r1Tl4yX5t2lT9Py0yEkhNVAOJEgj+bTY+aNHNJKGi99O6ueH+Bm12vh/ArwXwU/Nfv37+2rVeYOXJONmidJmM9xHesi2i1VqVTtqlHpPFVt1RiezUDqHSCetZXc1kvG1pLftcPIoYSQT/Z82OhL4FPNag9Qj/FOXLPKCtkRgfPILsAD2MRoQ0tge1NZI8p3Zdbc7ZyXOSzQQQPn8frSseClWfk7L7zMPCNowEJCGdj7imSUI6n6GbDXcBYtRAuKxAq5K1QK1mJzxP31qspx+ghT2lBTpHoiQ0tkdc6azgOryH9aj19M8D+N7Oe7mWcElpYWfc+UVLwX4pyhPo1z9/rCs8zDxId5ALpj+tFkjrXnR+WOQ2v9JDqLXtHI2MGD19mDmnhN3sc1k4QLy+X03tZscJ6GbATL07Q0o6hGlKXNPO6vqZbialsfEF6Y/VlVtatxEYeqSAwrgDONcCiZ+T1WGIbOj2uNuoHEEshzrLhkLeDG0ExktobBYepm18kPJlBNbTj+2Xm98TJG5h87/7yHWQIDBNzU54nsZmHsjZkTRnqVFuurEFBxh8Y4WKGmO+O4Twk8aYf7T0/RDCn+izrWs9szRaCqD+spVqKR6dqD378jqvq7cobdflhvBJQ+0epx9yD6HyENTtvo515ZPmyddzgeTucWcub8/bvKd9PKLZ+eL23O8NeAzZkdD5gMcav2fWGV1yFNO32vsdlZ/jU2E+uykRGq48ilTLjWfOkHUyPU5pqNAjfLlVV4oYhTNkR2LljAcQIyfR1pwjRshWzhLjgxayk4wEBFbZj2hgBE0Umx43PGB8MGQa2+M5O48gO0OY5mbnifyed7DOrv4/D+AnAfzuwvcCgKvZeYElp0M9Jgxlc9ylL5kzDrbU1euUk69GuNpaoF6CaTEnv3IIlR5m8qHjTAsknTSHaAe7X5rPxWnj8OReH6kba3t88/D8S+ZUs+Ofb9CMMbAG51og8iRfE4obf778fbERxunzQdisnz4nhfdvL43Rg0i1eMhSe+6Im8n082fDvE40wSevrzMDLHx1eLPQzZ5oHhARoxZSYrygicpISb2JktCsHjNUENR9oImS5NbkENRWc5abkmdc0xLC1ceN7ayJchjyff65rGazE0L4vvnPf/DTbOdakqWdhHaz/Pya6FD05kFsLfuodolN1+lLExTTdcgc9/V+h8IUapwRmGfpZue5QM9TwoB4SGrVTbWfPXwBDzjICfd8a9js9sr3kiOIJ8hOp0n+KPwcnyHV4pDOB90wn9diPnbIlz5/e1hEAx0s+ofHkL5nkdRgBtzgqqHOyPkyzwnHPSyGJrLzfG5NprE1tDU5y+UZhOABQ4XFyvn5us1mR+JCZp7QwDyD7GQt0EkzaSDSGIXKdfA+YICDMzd8XrjO46Gif/KRr13r61nySWifl9fXFf4pPsycWp9KOe5o1tVbZXNpgvlwcHLo6NVMynM/yt93AooVEBGu82BKQbNjTZMSBsR77VlaY67dzP7wYjTqXED/GnSos8O4GEE8eT5orNNbdcWI0YPNGfu5I637qNbq+brzz5Mt+h/WeD5ZN9HYqgOR5Jr2LLID+6BFtMSggKspSW5s9Lr23KDACtzNHrHgXgT/Ai1Qw/hgkLixJdZAJVR09B43OHjzubU6j7uxba6mMWYA8C387VxLsjRhjwCfFnYW0il19Xp4EkrWJsgRoxMjgV40NuHE8uwQmv57Uu3HeaK7tJmsaTRkzc6Z2H90z2fAAPF+P6OxOQHdDHjEQU5hfEBvUh/7vL0aYlQbAmis04H682GUNmcn16E/ss7WGOmQdT5idIJECQ2DvBkwwFfrJlH5U/kyc92WZic1O8+FlZ6HoOYDtYDG1kKMJCGdjxgqyGhs54YKQy+6GSY4WOAJ5sJwojGaXMB9RnY+t9X8lBtjfrcx5i8B+E3GmJ9PfwH4BQA/8Ul2eK3T1YsOJRZ4n3DGtdz5U62KWAtU/r5YY3SGRKmNBE5cnKT3Q004LrZyPtcCGdODJvh8tkysa05zNCSNw3BifADMLxvBnlsOciGEWWckQ7nYWoozLdAofT48iiAK81pq+xUj66fPyT5aIHnzkH6ee8g/G2KJkfWHEXsyEiVsUoOxuD2A7DzlboZIjxuazUMS0D83zY/GB2RaWDYSeEQLJDASaNQdNDS2TohRrW4IATY8j8DYW7uJmnzAYBz8Z0diOzco+BEAfxTAf4Btrs4vhhD+n267utZTSzvJP3OrkdY9m1jSJ7dCq+GzED49YtQ+zPQK9+MbHygRxIYg/dnf2XofrUOHFNlJP/+h8PPiLBxT32taU42vf7Ju1p4exsVaIHI+Uqxb1wJpaWz98r0+LVItzcs6R9alh/yzvCHZ/XA2xOo1HJM+f8/eb1KDmITA1Orm3BqhG5v3Ib87tnUFSAnmENRWU+Kfp289hew8VbeTZuchetzz2ppsfNBoSu5wcLjhmd/amVX25DzucPCfWaAocILshBB+IYTwswD+iRDCz4UQfg7AAOC7ZyrbtV5gdT8cCJsH+sTyQSSKriFQGh+cctHJWis1J79K+9AhUa3DlwwpOTt0PB9MCZw3k1FALEGMHkB2FHqgOl0n3WeyumdNtbTuOa2x08T92boPajTYmsl+rnS6Qz7dBfJBQwV2qKiYpn2K2Mv2mwwKanWDhG6G2EQ1ESMJfQuARzsXCBI3tkfoZgJ3s2x80AorzXk4grotIwF4eJjnkLMTxGhy0UjAP0k3sydNn/MBN0xP130P69FP+U8aY77VGPMrEOlr/waAf7/ftvovY8yvM8b8D8aYP2uM+ZPGmN/4de9JuqTC21OBrJTG9rB70WtMLM9fijIL7ofzOcTCfO7E8tHm99mz7VnzO3lZU/IIDVOkrXmg6euhBQohyPVAjzQPIsSoUTcfbrk23OIhy6mmT5pb09Z+qF0rqy6FWi3m2XOyE7LeacgibSbZYcaP1n32+RtmZKdWV+RC9lTdJ13eTDu/R5ZbkwwK+tDNWsiOqG42KCgL/kMIGML0PC3sRLMzeY+7EdDYUiNXsbQefcAN/hvaoOAeQvh/AfxjAP6TEMJ3A/j7+m3rk6z/EMCPhBC+C8C/A+APf837Ea9R+NBO7456boKM4342+RJPQh/luHeiUbAT6LVuS+xE9zPNTnT0et7KOXP9q5odmU7lkaZa1kS1c4FGIWI0DG0tUPqyCI0yreZBZjUMJGe6M22YENlp3GeSutZGLRDdoOBEsyOlsZ1pgaQ0wUc1k2zkzEmRnZPmTIqsP0r3ffr9dopEyRD7MBsUVNFfJwjTxGJ8UGsmRU0JZmSn1ewIXMgAYMJwgsAojATYuUCmXdd5oeD/BIGZXIg25cK6dcQourGFb2BkJ+Gm/wCAH5//vu2h+sLLGPPXAvjbAPxn85f+GIBfY4z5zq9rT5olPzSf0c36IDtLU8LlSuvzOXrVrbuFAc8fZs5f4n2uw+Tlwnyg5Yqko7E1ESOVZqf+ApOgL+eCadnnGIifpTNjCUlTEt3Yyt+TurHluidaIJnGqO5KJ9XsPIwQCAX0p1rBTkg1O5dNa7hybhDTh+4rH2LVh4TWoKiPaa1gBgymgcBIhO6IltYDXHV4AwmigdREuYbGSNpE2SaNbRH8P5Ozk0JQW+5mkronTYmX0c3OkKjR+1j3WW1NNoA42e83mmZntX7cGPNnAPwWAP+dMeavAlA3AH/99asA/J8hxE95CCEA+HMAfvX6XzLG/KAx5i+kv37pl37pa9jq+ZJqKU5fXt2bB64WSHuYOc/9ILvd9dqvlpPfOIR2MRJwXqT7ONMCTdKm5BHESLXfM7qZbM9nNCtpQ3k+cSfT2ITIDtB2pVs0RrJJ/pmrIhspyWHRbG2NkhbG1mI+akXeKzT7abpvDhUtf38SUlGDsU1kR2okEB5Fdp51eZvDSqt1JdoaLLS72rIhucdJEJi6u9kQHByGp6ycz3J2Jh9EdLOzJiohRuHZuifXIVlPfyMjO78LwD8H4O8IIYyIJgXf121Xn2btP6GHOzyE8AdCCN+R/vqWb3nNaCGpMLTXIf/Rw3g3xIhNS+iElEgPM480qTcroZu1Xem02pomYqQyEih/fxLT2B5AjDTXodaUCIcL6WfOw3a5SMkotBoGTgwKhM8z4DFkhz8E0CHr51qgTjk7ZFc6uaHNgwgMu5mUajFz89vI9xI8H1JTUtuvyIUMC42ttl+RC9mqbu0+y03Jk02UexTZEdDYhkpdH4AbpudpYXMIam2/iRbmyM1OorE9jRid0di8x804hM8Q2Xno/yiEEIwxNwD/kjEmAPjxEML/2HdrXdefB/AdxphbCGEy8VT4qxDRnXe3NBx30+C4S12cHuVgP68x6jNhPc3vEU9Yz3Jgek1YpfbF5xoC6RT/rK4OMWrQzTpogZwiVDT9fGmNClpYS7Mj1WgAs2ZnOpmMCxu/0+cDGeGSDlnyc/Ikb+jpQ/OpFkjXlJzTJTs1JeQhlhZZb1ney/K95mFTta4sGDjl4dTqSpuShBjVkCipZifWdY1mR0ALwyPIjouj6acMCuYQ1Erd0fl4jcRNSd0MRKSBSYhRi7UAh2C/FNZtN1HTZ9jsPPT0MMZ8H4D/AsC3Afh2AH/MGPMv9txYzxVC+HkAfwrAb5u/9E8C+NnZZvvdLZUrkmlNWHV0qF5WrecvRe5+xZz8Tm5Aj+R+SA7jZ1qg0cuCKU8Tx8VISbuZjNeBj0SNwv3ak4mwdJIPJM3OmdGIrAE+te6V5vdUtUCyIUuqex6CSna7EyJGZ+5xWgMTdt7QmWZSqsU8Q9bFz8lUt9FMap4PrSZVgqIiITCVulIaG06QHZHgHwnZCdXPhUjwjyVvKFTeF0OY4GCfo5shaYHqzYPMSKBNY3NeiMCc0s38XJdMY/Ofr0HBo/9HvwvA3x5C+EsAYIz5YQD/DYD/qNfGPsH6nQB+1BjzbwL4RQC//Wvej3hphMKDNQ9YI/exXJYmV7O56Gf0rZ40ComQ9ZH8Hg2yU5+Eyg75j+QYfbg9b3XZK1S0u6V1B4OCwVpUyq4m7lxtjZQOtdSt0aFkQxYgucdxD/lAQs5OmkkyAqPVYvJpuW3N5Ch8/p4h62rEvjVk6WBN74TPnWBjU3KK7EiaEuMwVYYLotwarJCoFrJjIMjvsTPCBew/Un4W0Dtze9bMOVpl15oSFzAYeVNSc48bU1MiNBKoGhS4gF8moZudurElxOgbt9lBanTS3890tne7Qgj/K4C/9+veB2Mtk9BO+RxC+tbXhhg9e+g4DQ3spTGShlOeTVh1h/zWYVEn+K9z/WVi9JOm2nnRhLV1HZYsHAUSdeJCJqXenSJG9JwdTXPW0hjJDvlpL+fNgxDhqrzx1Ej1qStdn+evVIvJRtbPkR3Z++IRxEhK7WzVnYSIMk40O1aYs4O5ifrqDNl5mm5mMWCq7ncI0/N0Myy0u8l7DLs9JQRGkgPjMdTpZt7L6p7Q2DLdzHxBrStGjHIIat1Q4Qb39O/sPaxHn3Y/bYz5YWPMtxtjvs0Y80MAfqbnxq71+NIIetvhftKXV/zzVAsktlxuc/2fbfqGEy2FuO4DtDAZtehswqqbWLbyRHrQPvQW0fXfm0zkXv+9qRqHs1wVxWG89TnWIEYtpERqnQ7Exq8XYlSlSyryhtoao15aIKkWM/55/px8fr/rn6/VFWsbq+GqMsOVR5BUFQLeaNYln4lg2+GfUlpY0sDUPheDGDG6Nfc7wMHDPN1EteommpV/fE6/qmurCExsHiYBstM2KHBpv1LEqBJWOia62bNNSWrmGtbTnyuy8+gn8vsB/FoAPzX/9evnr13rBdbiKiOdsJ7Rt557cBtjurgtPeoe96xg+pEJqxHQzR7Zbw8ERmpQcDaxdMLm4VSzo0aM6nVln4m6Fkh6oAMecf2T07ea2julVuXc/ZCrBdJqjKrNukYLNNjzJpWtmfTpOfksAnOC7Git6c+Q9adDqHFSV6iR64SsP4QYCfYLM0TnrkpdI7WINgNuLcRITGOzcb+F53oIATYINDCIlta3arMTcDMOTnAY9xgwhLIWKNLNvKApMdE9rkpjC7O72ZM6qweRnedpbEljVBmETA53457Xhb2DdXqljDG/CcB3Afg9IYTv7b+laz27dO5FHSesZ4cZcm5CvzBNbVPSgLgVNKsWDUiVA9OYsH7rlzL7YqCtXeqjBZK5IqVfSWkIoPmsPUrDFFHvBoMQ4mdj35RrtCptxKhzXaHJRt1FT0bfAtpud1pk/QyJYlvTS+mSvQxXTl0rfS+NnPA5eaoxEtLY7IDBBEyufHDOB2rBNL9lfGCFdDOYAdaEIjKZ6WZPK2vaeUNZUyKo680Aa3xRC5TrmucP+a5Bj8tNydP0uLkpabjHiehmZzS2SWZv/h5W8wlijPkBAD8B4F8H8D8ZY77nk+zqWk8tjbVs021JGMKX6p5y8skTy15J29pwSjay8wh3vkcTJW8e+iBGLc2O9yFmJwgHAGlf++VyQ9IB4fLyQ3OrUV2QEq4WSDrJB9qanazREGuMzkJQpZqddlMi1QKxDQoeeZ6t/z1WXbnhSvyzqRXsgOz0coGUGpikKb2v2BQOUutpe0Jjg8wiOufslJ45TkgLwwmNTSr4xwqJqiFGmIR1h2p+T2pKpHSzGo1tMRJ43pkvrkrekEuBrd9gzQ6AHwDwN4cQ/m4AvxXAv9p/S9d6do1CbQ1wptnpM7ntpQVKX3/2LJNfio0cGE2IZAuJYgvoU12NFshVXrbO66ynW/QiqRgdKB++lkad20yOCqrZeY6RTvAPVKh3irDS5udYQTd7BNkR0cIecKVja6K0uUBnIZ1PC/MfGIYAMo1RrMtGqvs0D48gqVLq4VldGY0tPX/fyt8WNjuJHlfabwgBQ3BwGJ62cl5odyUE3ONmBLk1eKQpkRsUVBEjH2lsEstlD1vNBVo0MEIaW6VuQozkyE65rp9kQbDvYZ190scQwl8AgBDCnwbwzf23dK1nl2bCehsecFsia4GkeSJnWqBx1miIhaxV+pbM1esciVKGfzYOobImKv18i/bBp5PotUvH+ywHXpK1QJrP2mmOkYbG1jiALYfxXsMQrhZIYz3ddqVT1B0eye8RPifJBgVpL6eIkTjnrPz9yQfRcOGseZDSws5CRaUGJmcIrdTIJU3dQ0U8PqSvCzQ7Fr44dEvCfJG2xtgZgTl+LyEPsrqzUUMVMRII/tf7rdWVWDkjIly1pmRBYJ7Nw4khqFXEaJowmCBudqo5O9PcaH+GyM7Z/9EXxpjfgMjoPPxzCOHP9NzctR5bo+KlOLS46MKmJP7MIy5OfC1QF6G7ECk5p2/JXoqnE1YnfYmfcOeF2prWoSPRzdjNpHaKX6+rO4DGuh0sohs6AqnVMBCvsQ8VLZAy3+vcrVH22WiFSMa6smby1O1OSIlqGXfEurJnWkuzIzFcObeQ70NzFSPrOVS0vt9v+vD8oe4MqZYaH6QDppvK1CWxZsfWDQomHyLaIWoebjNidLy+yS1M1pTM+y1qgRIt7Mun6/q5bk1jdMOESdTsRCSq9oy8Qyb497BVLVCQIjCmrdkJCVX8DJGds9/sLwPwJ3ZfS/8cAPwN9B1d6+nlhHSH9DNfG41NPLnlvhTP3Yt0dLMmjaKD0D0hXNK6VYSrA91Mo6Nocf21qGS1rpKyBDQmwkpaGFCj3qVJvq5Rtdj+vI5udq6tkecNdaLdnTUlL5Jzdl7Xd6NviZ4PZ4J/KbKeQ0XL35fTfc+ZAFKDAgAIvtzsDGGCg82/h2fqWhOKWqDRCfNlAMBaWASUPsZiYT5mZMd4TEX9YcAXwv22EaNIYxuF7nGp7v4ZGZtJWW6NM0OVbjZlbY3Q+KBmqDA32uYbzY0thPCdn2gf11IsqZAViC/xt0q0ss6goO7yJs1NAM41BJJrsGiB+kwsaxPW0Xt804fnH9rnWiAlwtWaWGqQqMLLVocOPGIRzaXdae2WgZZmR7Pn1LAXrrEC2Vk3UffdreoUjeojNDZpU3KGlEifkx8rz0ktje2T5yMpNYhNFzIJGveAlbPIwCTXbVCMuhgfSJGd+AFzBTe21Dw4c3vahyw1HJM7NlHOB9yMzEggmFsO/9yvTDeTCP5zCGoZWf9mId0suryNVZdNab6MN7dslb1/Rk5T1C6J9gtbNSjwmW4m0wLVrLLD3ESZbzQ3tmu9j6V5KbY1Ozpkp+W2JBJw4mQiLKRvJS0Qm0bxiEBWSi06r8ulb4UQxPkRTSMBBTrQrqvLnqrXlTckw3BOEwRkn7fWgVGr2anV1V7jHtqlh5ASIU2w/txRahtPml+2dklqpX/2nNQaCbS0NSrDlarGSIZwPZI3JEFRTUJ20tR+XVNBC2shRqMLsnyZue4AVxwuJBcyKY1ta+SqCAAAIABJREFUqBkUSF3IHqwroW8lLVDpueMmOS3Mm7rxQTISME/T2OL9XtPspCbq6brvYF3NzmewVNzuBhddY2l9xsmXvBTTXirv8EizktAHcHL4ktI+HhCkS/Z7GsKnzgUiH/Ib9K1edLPuBgUKDUFdiK045Dc1Ozo6FHBmfKDTAh3rKrVAJ2530tDWal3N762FRHkvMlwBZi1Q7ZAvpJvFul8DYiSksS3PyXpodg8kSopwJT2FLyA7izBfQDdLyE5BCxSbqEnsmjbA53fDtq5QmA8ArZyduS6ENLZa3aQxepoWhpUFd7HZkVs5e0QaWykE1UnzcOYQ1JrxgZ/RvwvZudZLLo2gt4WUjDN9i003k4ZIprqtCauIPoA+WqAzzY4T5tY8huxwJ6yaA2hrv05xUHykOWMbCWjMQM4m2Bq3sLbOSJ6z0278dDQroHav6YYsp82k8Dq0NBpAh/0K3Q/TXlqaKMk9BsSmj50bdib476bFFL4v0l5Kdb0PCEH2WUsHTF9wY0tuYRIjgVy32URJkJ1btJeuaIFuRhbSmehxtWfOXUg3C2bAYCp1J4+7ESJGsFXEyCnczXxjv8lIQKKt8ai7xwUpYvQO1tXsfAZrUhySoramVldGH4h1T2hh4pd4XQskzjfAOcIleok/wBnXHPJLU/EQwmyo0EeYLzko5glr4TCTDqCvdcCPP1NGovQI17lbWCeqoKphLx1odIgGcIaeCV0gT8I/pQfnMyMXtkGM9LlzVncSIiXAyXPdK59nLYRL9HmLf7ZyzkSNuqk/f0fFUChbTxdobBp3s4yC+ELdWZgvaR5SXVdozrJBgaSubSM7g4rGdobAcOsuSImMHlcLbc1GAiJ6XN34IIWK2uHD03VffV3NzmewsvBWOrFsIDsaukNrwio5eAHx4NxyGRIfDho5GqNwAmitgTV8usND1CLFYbxo+ZkOdOQJq+4A2rgOCrpZy0hAhXA1JsLr/54ow6el2SEYFNQcl9b/jqRua79S97izpkR6IG9pgTSGKy1kXYeAc+lmQPz8sxH7R6z/VVb6dC3QA88dCbJjzxEYibtZy/ggWS5LwjTTFMsVjA9GxX5T81DTYorCNNFuSiaFML9FY/NKGlstXNUnZEeyX9iGQcFFY7vWC6/ReVjzfG4C0DYokArSgbOJpYy+BZxMWJVaIPZLMdY9QaLI1CKtzirWqDt6aeyLy0JWXaMOVParpHbGutzDzBnXX0cVfMCgQHWw60PpKx5oFFqg9Nwp8dzVrmmN8E/pc7KlQRS7euEMgZEPhVp1pQYFj1haszVyyXCFjURNChQ1HzCLRgLJIlpGNwOWg+x6TS7gbmSuaQkFKdf1uGNCkNCh7K1qfDC5MOfWKIwPCnXDTDcT0+MqCIxG8J+tsos0NjndLBhb1QIFH/drbxeN7VovuMQhZogHsPZhvIe2po9AdhLmy5zV1SBcNSSKQTdruZBpOO6lh7bmANpGogj7LSZ4J2onNx+J0pBUtR8K5OERgwK2vbeCbtay79WZVrTsyHWaqFbOjqRZT3VbGsQehiua5681LSRKSPcd6rSwWFf2/M2fiRJ9Vuk0CpSRXw2NzZxYTw9wIgQm1fWlpsR7DPAyxMjUkZ0UVgqRG1ukb5UZBhOsCc8L87FYWpfqLvky8v2W7rN0bYygeXgEMbK35+lmbtYClT5uYXJz3avZudYLrrGTtmYU5rUsdcvfE4eu4WS/KoMC2wXhqiE7KgpQgzOuTV1f721bt1NIp8IprOW2xNCpNJEo1XWoBLqptHcPNGgK3UOLgig5kLcQIwYSVWzYFfleLQMTDd3sNI+sx1BIYVBwihj1sOhX0s2ahitkq+wFWZfk7NSRnWw9LUJgkhbo2ERFutkk0qosiFGprtxIAPaGwQT4wrPBK7Q1mJGS0nNdh8DcYE0Z2UmIkaqJKiI7crpZy1AhGR/Yy6DgWq+4pC44wPkEUEdjq7sBaZCoVlMifYlbexI+R76+GrvapAVqUos0B9CKlWisS9YCqRCjhhaIcJhpaZdEdYd6XUBH6WsFKOoatNmsoQPdLNY9fo9iAlFpznrQclXPs4YGUW1Q0DBqEDdRwwnCJQp1rt+7GrpZizaq+UwsqGTZhQyQNepmmBGYah6OrHloIjsag4Jm3YhESfJl0ApBVeTWBGNxq2iBkjBftF9rZ21N4f5Ngn8BUtLSGOW6Qnpcte58713IzrVecmloCUmzU+Jv9jIo0DQPtxOBrJTG1nKl0yJcTboZWQvUD9mRI1Hp/7GZs0MXuetpKm2XN8Eh6UyzQzBVaFH62BRER7gnysiOIqy0odOYvI5u1tIC9dD0ifNaMD93ak21l+eR3axtG7moEJhSTcZngoustz9rGmpnHYFJ1tMSulludkKZbiZ3TauHlU7O42a8KLcmQfYlLVByTTOiutHSujS4SXWtyKDgNmtrCt9T0eNmzU4JMcpNlMJQoTS0mOsOAnrcq6+r2fkMlualmG00S/e9AikZmkYCHSesqsPM8WkVDzhCK1E0kB3FSxGoT4QpBgWVxhd4R/bFhOvQdnmTI1F1IwzFNR4SysWlhTWzjBT7bWl2Rhfk+V5NnYbuuQOUn5PdECOFRf+Z9bT8fdF2TdMNWcrP3/W/88x6RBfGRqI0n+FMCytYOY8+0cIUdLNiqGjAHZNI8J8O8EWNkfs4/7fl+y3VzTQ2EWJkYWvIjrJuRIwKyM4s+JdodiIS5YrDhdQISiyiPeoao0ShHC4a27VecUldcIBz7nyvnB3phPUs/FOO7LQRGDbCpTmAxp8rN5Mag4KzqXisqzjkkwX/j2mMyG5LnfYLrKbYGh0BGY16qPHTUAVrdDPFcweo6ykkhhXrusXnZLfnmRypPkPW5Tk75edOopuJ6FumbtG/0HK5n4lR8fxNn/3SJF/zGbYzjQ0NWpgEgUloRQglxGg2KBA0JaaB7Phx/pqCxlaix2noZsHeovNcUwPzfPOQaGElSm5CdiRNSchGAg1tjYYe10B2JM3Zq6+r2fkMlio3IXPyS3V1NDZ2HsNSt6YFklta11zpNJPF9HNFIwFN+BzOtUCil+2sBSq6ymjoW0nk3tLskA/i6TAj0y7NKEnDxUl0+DrR7GgMCpqNqjKkc723TV0NEtXQAmmGLE0kSuXWWG/YVch60yBGZ1BQD+nUIPZlzY5mGJLrfko6qmYY0kJ2FPs1DWRnmpEdCQKzhJUWtECTx90I67Y0Owphfquuhm62aIGO13fRwMj2a2vIjqJuorEV3VznazOImx1XdlWcG9ebgB736utqdj6DxZiE1iaWKs54heOuscquTSy9D/BB/rKtaYE0uQlAfXKrCThs1dXQzYD69V0mlvLmoXhI6oVmEJCdFq1G8rk40+yorKez4L/RoKkOjMfvaehmZ8GM4udDE53UDFlSjcp+Va6V9eekJo9sqogQJ40GsRK+vARbs+m+cmS9TcslfCaaLm+C50NGYMrIjtigYKi7pjmXBP9yGlupidIgMEvTd6wbNG5suYkaD9/yCheyYGL4Z3G44OUW0bFuWWMEhZFAcnkr9TrpmksQrldfV7PzGaxJ6FYDnORzKCasiS9d+pxqrLIHayvOUHIXsli3/LLNuQlkLZDWoKA2EV6QKO5E+N1aWvfKMVIJsduaHZEYe2gf8uN/X0M3Kzd+GoQg1divySvoWw3Nji4Pp45ERbcwvhZIY7hSe56FEFQIVw0B1+TLALFJbQbMktFOlfX/I1pBwXPStAwKvJ9pbFy6WcqXkVo5A4AvIFFJAyOxRm42Jal5kBzG5ybKT4X9Zhqb3NK6NARIdUXuZg1kJzc7Es3ObKhQRHZS4ypB5F58Xc3OZ7A09K3TSaiS4161gFUgUWyqzmN1uUiJxkgg/RzbUjXWLecNacTorSl+ppspmii2Jqqdz9Gn6QPW03FyQ6m4Fm0jAV3o5Xpv27p6DWLNZEODVAP1BliTWxPrVpB1VVhp47mjeE42EVoy7S47/glNMIypoZ0ExIiMrGcEpmRQ4ALucCJaWEIrSpbWbkz5MnLEqEQ3S/QtGT1uNigoNSWaJmoOQS250mGSu5vBnmtgRIiRHWZL63pdCd0sWXCXeh3jFb+3F19Xs/MZLBXto0F/0ebsAMcXQqKbsbVAmnBKoD6x1NASgLor3Ug4HDSREhXCVZjia4S36V5oaIF0E9a6nkSV1VJEBxTIzoM5O3RzCedhzfJ7eGb1opudGSpoNYg1CqIG8Uw19kuXs9MJ4aohtAotW6rb5zlZpsdpDAqAOmLU3a1R8tyZD/CmZOU8TbAmIIjoZnXjA01TYhqGCktT8jzykPfboLGJkJKEGBVc6YKGbmYHDCbAlWijCmQnW08XkR359YUZYE0Z2cn33uXGdq1XXKPT5Ca0J5YaTUmsu/1AsegOe467VvAfuegtwamOk3+oq91vDYkiCIXLRgL6/bK5861QUV3dNuoQ63KFzeuv0xs/1TCkZSSgO4wDdQqi+NDcaPp0BgVnz0nd83d/HRLdjJ3vlb4mRqLOnpMKmiCbjhrrngyF2LRcxXMy5+GU3M1yvoy8KSkhRk5B3zINGptTCPOX61BAdrKmRGOVXWp2Ei1MUHe+DuW6KbdG1pxVEaN0bSTGB3PdosYoKGiNL76uZuczWKpJaEOzo03wLtVVu5sNZY67xoUs7seWMzR6aYEUlCUgaXbqWiA2119jqdqqq5qENlFJ+WGmNW2ncP0rmh0Npa9ph+v7DEO0uVaxRsXyXmE0AtSc//RGLlUEXKkFOiDg8z9K6FuxrkEIx/1qPhdAAwEnGLm08724wybGkIV9HfJBu4RKzu5mkkNoy3raTwqDgrzfgrtZRnYUuUCl5mGSGwksIajH/WYam8Qi2tbd7nJujRDZuZ0gOxJEbnFjO9bNyI4ktPXF19XsfAbLKXITapPQEMJ86OBOQrWH/NokVHvIj5PQeugl2z1O4+oF1Ce3WoOCKie/GxKlNyj4lJodnXapjkQB8SBqjLSRqtO3YohkB5qV62RQoND0NTOSCHTf2mdOi+wcn5P6IUu5bqempLuVvpCefEq7k4SKxj9b+5U8f5fmoZO7WZFuJnffMg2LaK/Ia2nVXVzIZPStWl0NApP2W0J2Fs2OxFChbnywNCWyENSh0kRZL7/PXn1dzc5nsEaFQUEtR0OLlNwq9BeN+xZQPySlB4L0kH+Ws9ONRqHQArFthmPdPsYHdSRKjmY0Dx2MfI6Gxa6I/tJwCku1NdoEoIXA9KK5culbsa4m9LLPfls0Qc3zt/o8I9BRgWPTpznkx7ondDMy8pvzveg0YnkTZYxpPCflTeqQNDAFBMYphPm2UXexXBbUtXXEKGlVRLS7BgKTGjYR3WyoN30qbU1uJgvXITclslygGt3MKOp6c2sgRk5c99XX1ex8BotDozjWBPRNyf7QkQWyZC3QUpeLPGgtoqs0CoIrEttmGKhrdpy6+a3VletUjDFdrkMLgZk0h5lGQ5K+zh4CAAnZUSIlZGF+K6RTQ8ttZRkxDAr2v7uYkaNvSo7DGz1SDRSev71ywxSIJ/AIfZY9bFK+307ofDLr6fmgXUJ2kmuayHq67vKmops1modkfDAIEJhmU6IwEmiFlS50M8F+s2bnaJW9CP6FYaUmFENQM41NVNdWLa3tpdm51quuRDcTv7yGcvOgdcGpTW61Atn6JFR5yB8qHHe1FqgPslOlZ/QW9GqQqJZBgSYXiIxEpa2Up/gKZKfRkABQWcgvmh2utqbtxqahmy172y+NVrCl2WFol6r0WXpd7XNnbiZd5XlG1wLpkPVuTUmNlqvULlXrami5GYE5Pne8wiUroyCFQ/Mi+JfUTa5pJcRIT2MraoycXAPTbPqc3uWtqAVS0c3qWiCrqnubQ0ULiFFQNGcvvq5m550vbW5C1UhAkW8ALFz/42SxL41Nm6Oxf4H11gJpEC62ViX9XMs17b3kGGkOMy1kJ38uyFogQEffamd/KEIkG/QtVZhxA9nRuJC1NTsMS2su3az2nNTTZ9P+dgi4S0iq8j6r0OPEyM5JWCnbejqbgbyQ8Uw+aJcslzXuZulnCs3DIviXu5uVwkpVdLOGxgiEpqSE7GhoYUsuELduaNTNv0uyQYG9aGzXetWlPdzWqDXLS6bPxFKOGJUPHQwXslJdRlge29Ur/RzbhSzWrXDyuwmQ+9BfVEGa848UkSjFfs81Owqk5CRnR5uHU2tU9VpBblNS22/K95J/LlCsy6Kb7ZtJPX12fk4ehje6Q/45EsVGYJRN39DrOcl/ri/5Mq0wzecP+RkxKjUlCsF/C9mBj02UisZW2C+8nB5nMwJTah7kTYlp1DUKwb/JuucGsiN0pashOzYkl7eLxnatF1val2KNk8845AMNulknLZB4Ylk5LDKakh6H/KrGSD2xrNsBx7rs/B4dXaeGnGlCDrMWqGVQILHKfkCz0wPZYVjIf8qcnVFxHWwly0g7vDlDdvS03O3XWQYF9ecvV7vEeF+UP2/K/XYIFQXqiLJq6GYaRgKTnBaWjASadDMJja2J7MT/lgSBaRkUJCqeyHq61UQ5XfMAlHOBNAhM+hlfoB8aRXOGOQS1lJdl0n4vN7ZrvdpSh14OZy8v7YR1T6PQ0bfOEBitdolvaR3ze/aHxX65NQRkp+lCJr8fatSi+N9VaIEK2TLaz8WZZa0I2WlQwgAlUtLQ7IwKwf9ZcKv4/j1DosiaHYYl+7pOWloXsvpzkoOs768Dw60ROKKTGsQz1S0+HwhNSS0bCdAN3WqmHbGu4H6YD66mpClJwnzB4dbeGq5pE4EeV0R2Ul1FLlDpkK/Il8lNVOmQr6CbtWh3mv0utLuj8YHVNDs5DLaAGOW6V87OtV5sMV4yQEvwr+X695mEHpEdvQsZ0ODksznuBO1SCdFg5Pc0Bb30Sai2+S0jUYw8kRZ1yxiNZqf8fY0L2RndTH7/xj/pLm+5Odv+7ha6mQ6B2R+caYd88jCk9pzUauSqz18SE2D/jFi0bNw8Mu17qPacVL8vaoiR5vo2aGzJNU1EN8vIDteFzDZygeD0uTXl/cpdyJJrWmocN9/T0LcaoaIqulmrKVHS2ADATcf7bLjc2K71qktNs6pMWBn0omJdgpUz0MOgIE2wy9x5fjOpO+TfrCkiGr2sWrVW2Wd0Ps3vjR0aCLQnzXrKUh3ZkdIEa/QtQGdQMDSQHQ3tbvkcH2vG73d67nSiz7I1UYwQX6DQlKT3Bd36v6/1tMYIo0duWJf9tmhsOQdGkbNTeDYE1ye/R2NQkJGdpruZAuFqIjvPIxrp2pVobEZBY1ssuBt0M1Fzlq5vCTG6aGzXetHFo4WxD/nlUFH9y6s9YRUjUZV8Dm2+zFmOhubgXJxYOt0hv4ZoLNeBvV+CFqgDJ7+m2RmdF1OLjDGwpmyLDPSjm03eq5uHmgkEu/HTHvJrn2MaQkumm9Wekwz6FlB4Tio1fadIlOI+62E9fYpUK+jf9OHNfNA2xZBORR5OOmiX6iZk5y5HdkpNSaJvmUFjUMC1cjamfsg3CqTEtCyiGXSzJo1NQDfLdVvIzuXGdq0XW2rXnkwn2X5drQXqNLFckKjthrUW3DWuf86PYIf7ZWSHrAVS1h2sKWp2luaXi0RlWqPmMFOhk1izHM6eXa3cD+k9BtSRKG3tdP1KOgLn+GGlIQQ4r0CMai6QarfGmlaF5Zq2/brWGOUs/FOtiTo8z5SH/Mr17e3WKL0OtvI8Y7iYsl0gc7PT0uwImod0uDUNS+tBk99T3K8cIbBNGpuiKRnqxgea3JqWoYKGbrZojBpNicZQYdeceR9wg4ODBQQ07VdfV7PzzpfetSe5DO0nrLqXwZlmR/ySqRkJqF3I5sPinuuvzXnoNAnN17eqBSKH5SnyZdLPtZEo+X3Woylpcf2lB+ZUt5azMylc01qandFrwj/bTYl4kl/5HKvpZkP5c6E1KKgjOzrk4UxbI24mq4Yr2mFIP03Up0V2dHXPrbLlBgUtWtggoLHluqVpU6bHcV3eFmG+hBYW91IyVLAEulkRiWLQzYp19fk9ZSMBjctbudkZvY+W1ObzMycArmbn3a9lAkjW7JC0NeyJZV2zQ5pYkrVAy2GxTH9Rc/KrtDsFfYvsQhbrVrjzBO1S1b5YeA1iXVs0EtBQt2Ld8n4BnQtZjb4V6yoQo050s5qVM+P+LdVlGRRU88jIyK9WI1fT7GiRktp+1c1kFSlRGhSc1u3jhil69phzGpvEoGChx9UF/yLr6YZBgVEgJVkLVHRjU9RNDVLJNS0ojA8abmwaGptpGB9ojARMpdmZXMAdE5z5/ChswNXsvPvFyq1hJ4PX83v6HJK0TV/tsNjboICtBRoZE8uG8FbuDlVzCvMwRosYlTRG8iDNVLck9tc4mwFxkl/T7ExOL/jfX4sQwmwk0Ie+xR6GqI0wqoYrnEM+23r69Dmp1ALt72Gtoc0pEqWhjZZCfAkaz09qUKC5fxuanYQaSNzNYOa9FHNg9IdmhAJipKBvDQ3jA6Ogb2UEplTXywX/iR5XQmAGheA/1901JSEEDHDwsPHF+uyyZSRqcpHG5q9m51qvuFgcd36+TPswwz7ULc2Zcr+fKp+D5LbUw4Lb+YBQoQFpEt1rgZfSezftp3aYke4VQDXkcHQ6GlsL2dEYFNQ0OzyDjRp9S+sexx6GzHUdl25WH7L0eU5qn2fpx9g5O2f5PVq3xv1zh4GsNy2i2e6Smvs3NzsltzB5bk3O7ynVdQqhe4PGZlW5NTONjVy3ZaigEea36Hw6ZCfRGnd0Mzc3O0K6WUZ2fIHGZi4a27VedLE47jVaAvuQlP6ZPlnUHvLTYfFA+1DSBE+QKLHVcDe6TuLkb7+uNShoTVg1tLDWoUP6mWjW9XotUFWzo0CNTg/NZM2OOvSy8txRD2+yZmf79V7DG61BwWkzKX6etZszNWJfo8cpm9T9R45iId8wElAhUWyDggdobDI3tnMam0boXq4rP+QnZKdkqKDRwLTyhqwGMWrSzRSIUdLs7Oh8k/e4w8FBiMBkGtsR2bljupCda73mUruQdaRDAY3DDFmrQptYVhEuXShjVeCt3m+5mdRrl8qHL2lf0tICaZuSmvW0hm7WsqzVIEa165DoZr1omGyaKyN3CShZLrNorjX6Fjmkk3TIryG06ufkwXBFd8ivPyd1TWpN28igm7U0iJp8ulqALyBszlrW04RwyhLdTJXX0sgF0tDNWi5vmqakXVdjUJDc2I50M4tUV2CoUEGM9MhOWWM0ZYOCq9m51guuUTNJwvvluPNzayqudJ3oL441Ca1Zhisn+aVD3X0wMEJLyppr2qjQqQB1bY0mSHOpW0N25HVtZb/L0IKMPKgt5M8Qgk77JT/P+g1D+g5vtM/Jerjqa7nS1Z47WiZANRyYQLsrhw4rmtT5EGpbzY6CblZqokzKcFG4hZnC80xFY0tNSbGJ6uOapqGbZWphoSmJCMwgsnKuaXacD7gbubamRmOb5v2Gq9m51isuvZD1E3Pcu2lVdFqgGsedJcSuTsbVtKXdJLRb80tASoqcfC1SYqsGBVrXtJJmR2tQUK2rvX8rSMlinc5ufnX32YJEVSzvtYfxWg4M3UhA25T0aSbT53h/HbQGBbmJOtTluHfWEXu5YUXRjp2BGBWS7RMtVzQUatDYFitnCd3MzjUa7mYCF7Isji/Q2BhhmiUEZlBcB2vr9DhNbo2t5OxM3uOmcDer5fdMzkcjAWHwZzaWKNDjbpjEdV99Xc3OO1/al8En57gT3HWADpPFxHHvkFsD8A8zZ65I4sPMUDksKoIp1/vZnzucgrqV6hY5+UrEqJrfQ8jZKU2EteHAxphiNpCW5lpHCJTNekWzo7a8r+TsMPJaAD7drEZz1bo1nmqiOtHj6M9JrfX0YBBCgy7ZwXpa/DxrGBRoNDDNJkqT15LDSkv0OE2+TIseJ6eF2VtyY9vud0s34yFRkw+q3JqaocLola5pWQtUMj7wF7JzrddcWvrL18VxV08WPxXtrhPHXWtQ0KLVaOhm1cMMQasClDn5+vDPQvOg1ALVNDujGtmpIVG6wyJQzhTR0sJ6uTVWkSjaMIS73+V5Vh6yyA0KOiE7NSMBpz3kn+yXjSCqaZjt5lfT9BU1fV4xZJkP8CUam9FodjKNrZEvI0KM6nU12pqFHtdwTRMhMGWDgoVuZkV0s1reUKKFyelmtbozsiPV7FSsvZNBQZBQJd/Bupqdd74YoWuxzvsQsp5z3LlN1NiJ4z6SmtSj1bCOvtWiLWkO4q3mt4trmrIp6eXy1tIuAfL7Fyg3fr0MCrR0s/S7qQ4tlPQtdujlqdU7WbOz0BrJz19l3TPDFQ3dDOjgLlmzOJ+fk9KhUOu5I37+JqQEDcG/AimxKFlay3Ng2lbZfYwPrMJQwWaXt21dlxEYWVNihwbdzMhpbLmuO2qBbnAIYhpbvHYHZMd73IxDkPzO3sG6mp13vlhC1iNtiWRRWp3UkbVAnTnu+sPi9ussjnuJ/qLJrWlpdqSNL9BGuLQ5OzVOvi4PxxY5+RrHtFi3ptnR3b+pNj289iwPRzlkYWuBztzjtNqlKhLVKxxYiqzXNFHq5/r8/K1porTP9UO+F4cJUEKMuljea2iurTwcDY3NlmlWwFpbo8jZKdQdVHXLmp0Uprn5bz+xsgtZ2NPC/Gy53INu5hHECEyqe3RN0yA7SxN1ND7Q7PfV19XsvPOldkX6zDjuWu1S1SJa3ZQc6VvaySJQpuuokJKqZscr6WZlJEpLN6sdQrUIV12zo6fdtdyhVM50w3HPDPqWMcf7V92UVDU7HK0KO1+mikSRkDN23fw862W4Qg4dzs/1fVhpblK5mrNRObyJn2McDFdUNNf5kD+waWwm7sc2XcjkdUt0Mx2NrUznc0mrAruYIwjq7vc7zUiJHIFJtLsS3Uwu+LcV7dLY3P0IAAAgAElEQVSUkR0ZApONDw5hpXG/F7JzrZdco/KQ38ttqWokoKSTnLq8aSe3tbC8Dhx3LWUp7u94WNQcxlscdy3qABToRU5vUFCqqzUoKHHyQwjR9lN5HcpIlO7+BcrOU1r6VtpTLy3FEfntRLtTI8oo11XTwtrPSflzPYUDs6/DCRJF36+HNQsN9vm69fuMMbyh0nJT81Cim2nycNIhv1A3NxQaGluBdrcI/jU0tj2iEZEdaVNSQ6Im5zEYBY2tgkRFupmGHhev3dHlLUS6mVQLVEGiJiU97tXX1ey885Uso6VT7Dq9iGPVSndbOjvMsDn5aotSlOtqhKxoc/11E8uyYcXoOc1ZiV6kRUrKdfX7ZVOLanUB/WS8VlureajWVSO0Z5oSHe3uaMmeLLh1h/EaoqzVAu0Zk1ojl1ON0Ys916vPSYKBSa2u6jMx/yh1yGIMHGwR2bGUnJ1Ss6Onx+3rhhBWNDYF7e7AhvCz4F9Is6q40o0+KGls92LdhETJm5KyVXYyKJAaCeTmrBAqeoOX/c7ewbqanXe+tJPFavI6aWL56dyWdBPLGsd9dFoha4XjTuCMAxXhbQcEhiH4L9ft4/LGQLhqKImWdlfL54i12QiMHjEqOcip82U6aYFqIajafJlTJIqNgCu1QNXnOiFfBijRUUlIVAe6GXBEjOIwhEDLPTABdM91j6GCwMyuaSIaW6KFFVzTCJbWNbpZ/KZcs7NHjCLyIG9Kalqgxd2Mm4ez0MKkiFFFC6SlsdU0RvP1FWUuvYN1NTvvfGmnzZ+c494pVNRp61Y47k758qrSwpR5La1DvsqgwNavA2O/JcMKlU4lH5KWr4UQZuRMp9nZ53OMSqoksHD994thUFDS7Ghd0wDAmj45UU0tkDgvq+8hn+5CdlKXrtlRP9frQwtjFEYCNWMULd2s4c6nGzahWFc7vPFmgA3uoAXSuJC13Ngy3UzlxlagWcHDYRBZOde0QNEtzCtobBUNTBLmSxGNjHCV64o1O9nSuhT+6cQITLXuTLsT3WPvYF3NzjtfvTjuesFpn7q1vAu9kLU8qWNNFkuHL81ksWblrDYoqE1C1Rz3Gq2GdehY7geWTgXYNqlanUqquz/gA3pXL6Cs2dEaFADx/5cdehl/1hzpW93oqJ3CgbtZT3fK2emkxdSGA1eHbsq6qfktMQx01M4KEqUc3nhjow3ybiCioptVEBg93axcNyEaWm1NDTESh16alkHBpAjpbCBGxgFK6+lyU6Kxnq5ZZY+wJlw0tmu95lInbWcE5ugWBvBdhrR0nSrHXSlkbR2S+hzy9aGXQO1woD/k85uSCl1HjXAdaUvaIFigfLhNB2atJqqW3wPoKXLscOBUt5pAT96vtm6NvsU65JeQVEA/ZKkiUfRcINZ14A5v6sYSyuFNL8SoqsXU7TcgNjv7+2xxN5M0JRYe5qCt2dLN5E1Ura66eSi6kBGakmJzphDmV3KBEi1MS2Mr7feuQHaGhOLt6vpRETD7DtbV7LzzxQrhq7ubccM/e3Lc+7xstRPA+n4Z9K3SfhnITgkxYlyH9eEghEA7JK3raoM0gfJhcQmC1R2SSm5sDIOCW6GRYhgUlPRLFOODIhKlo4XVw4wTwsU2ElDSzaoI+GxQwG76lIY2VXczrYV8I9+LMbwpud1pP2tAhZar2K83Q3QG291nC41NdsD1GA40tmluShysjG6W6FsoHfK9QltTNj6ITYkiB6aCGKXrIEaMbItuJqfH2VtZWxNDUOXNTjI+2BsUeJd0YReyc60XXOrJYoXbzeK4140PuFqg6OpFOIwXwvIomp3CIYlRtzhZJCAaJc0O59Cx1GUI/kt1GXSzUtiuc5yGZK8FAliNVEmzoxtaAPHgzHY/BCr7VQ5DkhbomLuUEC72kEW/X6BsCAIokJ2Gduk+yA1Xqs9f9fCmRp/l0M32z984vNF9JoByU61CdsyAAe6A7AyaPBxEelztkK9FSo51PW5GgcBUDBXyfskITKKbyTU7s8aocn3FtLCMnBXycIxcW5OND/Y0wSk2O+aisV3rFZdayFoT0JOErIeJ5bxf6XmxlnfhSIf8EsedgxgdD0ma/dY0Oyyr1lI+B+fQUaCbaQ4dpWaHgJIMhc/FSKJu7esC+kNz2lfVNU2l2SnUJVyL23BEolhaIDYSdWaVzX5OjspBQHsY0olu1mHIwqObFahWys9EqrOvq3NjizS2dV2fJvmA+OCc6HHrIUByIRNra3JTUmBZKIT5i/FBCdnRIDDzfguUfZVBQcXSenLR0loc0lnRArnpY/wbiakEFsRoX9dPb6q6r76uZuedr+xC9qJGAke3Ja+aLNbyLrSTxTr9RetC9qn3y3J52x++tIckHOouGSXc/TLQjJKWjYFmVA+3SvoWUEZgGAYFRc1ON2RH35yVkahOBgXdQlC1z/VyXhaL5srODasNWbTGByW3RiBpjDrQfZXXISI722ZnnOlQHjajCM8uZyKNbTO8cQnZ0Vk5H2lsnmIRvW921KGXFc1O1BhNYiOBKo3NeQzwMvvtVd19E+UnhakEFmRnn9+TaGz2orFd6xWXdrJY40qPymandahjHBZLeRddcmuUk8X0v0pN2kadO6+lm5UOSc4HhCDXD2zrbieLgP4gfqxL0JMU6jIahyptiYGUFJoHljNdaWgBKFGugmYn1WVrjLSmFbX8HjXdrCn4VwyFqhoYTq5V6Townr/FIYvqczzvr3D/qp6TLeMDDbJTaHaSMF+MwCA2UTe4HQIe6VtyupmJSNS+KckaGCFCkBCjfc6OD3G/SgRmX3ecrZyD9JBfseAencfdODlSYmrIztv8bSGyM2t2wt5YYlJkOb2DdTU773yx8i6OoZccIWuvQ36vyeJRs8MRspYOoZwwzYJ2SXXoiH+ur0M+gJIPSdqMqHpdHt1sex30upq6IJ1Dvatq5DSUySJipDvkA5X9khCjoyWwDtlJl6+eY6RDwI8uesrhTcVymUVz/VTDG73xTCX8k4Rwret6H+CDDp3NyE7YNzsKwT9mGpspNVGKZgeRdleib3FobOXwT62RQG2/YheyiqFCbh6UdQ+uaTOyY4TNmakgUZiRHWkT9erranbe+dJOFoF5IlwRhrKFrHoXspZWhXEYL0wAu7ibsXJ2ClogRn4EOV+miMAQ0IG2Zoe7Xy1ltFYXIDV+Rc2O7pBfr8vRArFpYfFnbVGjAciHN8YYDEXESHdPtAxMutBcvS43rG7Rrwwzrgr+lVpBs+xvW1dnUFAahjA0fcHYaFCwq6uyXMaCGO1pxKrmIdfdhqBO8361rmlDCdnRCP5rhgoz7U6u2UkGBTtamFbwn4wP9tqa5JqmbSZ3NDY3N1H2anau9YpLO1kEyhNW9WSxOgFk0R1KtAQCzao0AeyRW6NsSkp102SRgTxM/tjssLVAS13GfkvaGkLzu+O4s+oe81oIjV/LNU3ZANdydrRUwR4GBU0kirzfcaZvyTWI5aEFKxyYT8ut7FebG1YNFVXSzYbKc12NgB+RHW1DDSzIzr7u3Si0NVianb1BwV3jboayocLo4n71gv/jZ+JOaUqOdLMPximakkrekJYWVnGP80oaW61ucMq6L76uZuedL+1kMf5sOSFdS1FJdTZ1nSfRobZf79WU6K1P+whZS7SPkUA3K2pVvI7SCJQPSQyDgrLLmx7NaGmBOIfFCt1M2fgd66Z7gututvzudNP8Hlqg8n4ZphVlJIpBcy0hJX00JSwEfPt1LT2ulgvktHSzQt0QgtqNrZzvpWdZhNlIYJ8bNsCrkJ1y3ejypkV29nWTMF9j5RxDUI90s6ED3Wya82aC9JBfMxLQ5tZU9utdorFx9xvchexc64WXdrIIRD46e7KYOe6F/B7OIZQ7WcyH0ELOTg+rVr1m53hIYojR8yHJFQ75FG3N8rVsUNBJC6QNvATKNBUGwsV23wLi771m9a5FU/vst45EaQctNfe4HkiUzkEv/lkehnTQ7HhPGWLxQzrLiOfYwVCBZW9+qEtAZ6ORwB6BCWoaWzA26nMOdDOHIHULS/s1flc36GhhWBCj9Zp8RKLYNDY13axiPZ1za9TGB3vXtLf521xL6+zGdpP/3l55vXSzY4z5UWPMXzDG/M/zX79/9T1rjPl3jTE/Y4z5aWPMD+x+9t+av/czxpjft/ve7zDG/G/z937EGMVT5Gte2skiMHPcDwYFuqbEGFPUAmk1MLVJqFPut4bsaCeLpbrMySL9kF+gk7AoS7Hu8gIbOyEllIN44To4QtNXQqLW/6xHNPaHRU7jV8+t0WmBSlrB9D1N3ZIlu3YoNJSur3IolLRAbBpxPWdHG2ZcQ6I65ZxpEaMmQqujjB7qEtDZYCys2Wtr4iFf0zwElJGdSGOTT/I9bBGJik2JvG6ohqBOalrY3irba+lmFUOFkOhm0utQofOF1ETdlM3Orm42KJDWffH10s3OvP7tEMJvnv/63auv/zYAvxHAdwH4uwD8a8aYXw8Axpi/H8A/C+Bvmf+df8QY8w/P3/s1AH4fgN8C4G8E8NcB+B2f6n+GvbSTRWCehJI5zaluaRKqC9OMfxYnoR1ettrJYvOQT6YJUsTdBa0Kg75VOiSxKEv7uhnhYrimhf77BYguZAFbsTDJoKCm2dFqgUpIqrpuQbOjDXsE6tbeXZ6THY1cdNbpaNTlCv5DCPP7jft5o1A7C8OQkTBkCfZ2sIgeZ/qWSrNjy5qdwbhFwyGpW9ACOR/3KzYoAODn5myz38lhMEGdW7MPQU2IhlHn4ezqeiUtrGIk4DPdTItEbeuGvN8Psrovvt5Ds1Nb/wyAPxhCcCGE/xvAHwHwvavv/WgI4S+HED4C+I8Rmx8A+KcA/PEQwv8V4qngD66+9+6WNt8AqHPcNQcOIE1CuVz0et5Fp0koiXbX65Dfy4WstF9dMxn/LO+XrTFKVCiGFojbpNY0O4xGtex4x7nGNTc2nQNXGXkA9Pcw24Us1WW7KgLl5++oRUqq7mY6g4La81efh3McNjGfZ3taGNAB2SEMhWAiUuJ2zYNKmI/F+GA/FFIjRsk97oAYKRAYVNzjSAjMPmdnoZsJD/mz8cGRHjcjO0oEZt9EqS2icxO11+xcNLave/2gMeanjDH/pTHmN6++/qsB/Nzqn392/prme+9uTd6rJl9AY7KofInHukfXKdVkscJxp01CyZPFUl1WRglQo1EQBL3ueMhXaYGKoaK9ufO6g3is1b9JBXgGBcBeNJ0oiGRhfkbPdJSoUtNnjF5jVAodVh1CUdkvYyhUctHznCHLEZHjuJAVrf/pSLW+KWkOm8gaI8ZgIZjbIWdnzPkyCnqRKbim+dmgQEU3KyBG3uuMBDDnAu1c6RbLZSktLIagHpCdSWskkJqoHd3M6fJw6kYCqSkRNmc5v6fcRA2fKbLztbZwxpifAPAbKt/+WwH8HgB/MYTgjTHfA+C/Msb8uhDCL83/zvqJu3/CSL+33t8PAvjB9M+//Jf/8tq/+rUtxmRxsKaYQ6B9iZfdoThaIH7Sdp0ORUGiShaldCvn5Jqmp2cUr0On/TKayaIrkkq7dGzOWPbF+7rxn/tk+DDscG3JNY1lUFDQaGiHLLfB4P9749K3gEjhYueGAfE5wKabLblABfteMlKdhkIUzQ65KSkiRsTcsL3gP9bV0NiiQcF+iDUYrzIS8GbAzTh83D1/b3AYtXUPCIzH3SiMBOa6w0675MdkjawzPtgjMLl5ULqb2YORQKLH6TQ7eySKRWM7XIcZ6bGXZoe/Qgi/NYTw11T++vMhhP8jhNh+hhD+OIBfBPA3zT/+5wB856rcXz9/TfO9/f7+QAjhO9Jf3/It3yL+f+21WHSzvVYlctG1TVTBHYrAnbdV7jz75dWHZsVwIStZOXMO48dQ0ZHYlJToZt3CShmIUTFHo0fuUh8KIuOeuA1HLRAFiRrM0a3R64w7gDJSMipdFYEysqOlm8W6JWRHR8sFavlpWoOCekPNsIguIzud3CUZQ5ZCXVXzO9PYNi6QOV9Gg+wMB3pcMihg151m+pYYKQHgZ3rc5j6blPStue5eC6Q2KDgxEhA3DxW6GRyn7j5nB7PL23A1O59+GWO+Y/X3fw+AXwHgp+cv/VEAv9MYMxhj/mpEnc5/vvrebzfGfLMx5gsA/wKAH5u/98cAfI8x5leaaM3z/avvvbsV6QN8zc6odCGr19XT7vZ1QwgzwqWj1ABlChA9j4FwyC9aORMP+fukbW3dpqECmU7CQDNa++XkGO0n7rwD2F6EHOuyKYh6upktuLwx3CVLiIbW/TDV7TG8Ke2XQiMuXl9+bhjDhawo+GdoBVPdUKrLHY4xDApgbwf6VrSe1mprhgONLVlPM7Q1mxwjp7RyBuATna9QV4UYYTjQBBdkh0w3S4J/Md0sIW5l44NBWXdvfBBc3P/nGir66kqkHzXG/EoADsBfAfBPhxB+Yf7efwrg7wTwZ+d//v0hhP8FAEII/60x5o8A+NPz934shPBfz9/7340xPwTgJxGbvR8H8Ic/yf9Nh6XldgPxMDOOe5tHzqGDze1OdffuL4BWcJpqrTjYRLqDc6UDKPfQzJxYOten6VtrgSbC762MRBEQLlO6Drz74WD13snpjYFOruve5vcvhW5W0QJxtDXbr2mHIbGuwdt0bFK/9Ut9U1JEjLrQiJW5YQVhPuOQ30vwX9pvPy2Qfr+xKXG7utHKedRoYGwS/K+GQi7RzXQW0fv9uoyUyLUfYdYYrfeb8mXERgJYrq/zAfccN6OsO3+e9rlAyXqaTTdTIzsVJMp4fTP5yuul/69CCP9Q43sOwL/c+P7vBfB7K9/7QwD+kHqDL7Am5/HlXc65BaJTVmmyqG+iyvQMDrJzPDRrJouZ414KkWTQPgr0OF2qfcnKmUe742uX0nVYvsY0KJiKzSRXu0Q1KKjky+imzQUEhvDZqGWKUGhWB80Oa8hy1Bhpn2dFJIpBIy7kDVGGWDstkPMBISiHQtbAmJ0wv5c1PSNzqaNpB1AzXFHcD9lIYHt9te5mKCE7mW6mNyh429DCCHVRQIymWfAvRTSQ6HFhywzR0s0AONgDsrM0JV/Iilbye+zclMiRnTISlZudz9Sg4KVpbNc6XyNDIFvIu2DRzco5O1wtEOOQH+seDwfpv6epua4FLC9FFQWoZOVMoSy1BL1kJIoQVlqycuZYT5doKn0akvjPs7mE6hrHP7cH0fj3mo9GLXNIjTzMh/GwGwQwcsNKltb68OU+rpU3Ww51Vltl75ozxiEfOD7XR8LnuK294yLKDJprbs7Ibo1muGEwAdO0HebdtNoae7RyToJ/VRNlB1gTNs+cHKapQWDSftfuqNPH+J9UWCN7M8Aavz3vzMiOuClJdXfNGTzHNe2gBXJKq+xKE7U0O58nje1qdt75ooXlFQwKNId84HjoSJNFzYEu1q1oPxjNWeGQTw+1Iwj+yxqj191vSzzP1hg5BrLTQLh0WiAc6gKre43QSK1BjXHWaBhD0D3sKH3aQ3MJMWJoBct5ODoXslbdHvtlGc+wnfl61b0XkVTC82EoIeCEJirv94hwqZrfmWLk3KKnGKcp0s3UyI7bNA/ZylmJ7Nz2dbX5MijT47K72SBvSgJsdKBbPyS1TQmiFui2o92x6GZ7Gpu6KTmlsV3NzrVecPXK2ekRwjcSBN5AfPl58qEZqO+Xj2jwtB9lq9Y++T2Uum59SGKgGQUkinD4Wg7iR5SE4XZXciEDWJSdLXrGCL2MtbYDBoYGplfdoltjB8MVikHBTrPjfYAP+ufZ/jowhjexri3SzSiW1pt8L8ZQqD5k6ZVppbrP5mm+c8sB14/pEKqgFxU0O56AwKAQ/um1yMOqris0DxoBfcmoAVrracTmbI/smORudtfR2A7NjkuIHBvZmXR1X3xdzc47X70SvCfSxLIYukamqTDoA7mu4x7yjTGwpkI3I4nGc12mZqdkUECgfTgyLayk2WHZLR/qEu7fmmaHlVsDHCmT2s9FTbPDoM8C20aVY3lfQmD0NDZrjlqgkUS7K7mFUbRABbqZ9vrun2eM50O6hmyDgpLLG0cLVNfHqXLk5oNoEs1v/l5h5YzZ3WxDCyPQ2IIdZqvsdVOSmihdU2Lh87McAEKim911BgWx7pp2Nzc70qYEKW/IbfarRoxSCCr8huqbmxKpkUClibIZMXppKb94Xc3OO1+spmQ9ac6TRQK3e302YEzGU93yxLLPJFQ/aeZrjFpaIIp9cSFfhp6HQ5iEljQ7S+NAoISFUl3u7w1YDuMqulmxAeYhMHuKJ4M+C+ym4yT61nTQAhEQrqJmh3B9h92QhYVU7zQ7jOdD/HlbHrIQng+j2/7OAM6QZX24ZVhPp2s4FoZCKubCfBCdphWyw0BgSpodEgJz2+XhpOZBYyQQzDFcdUFgyHV90uwoDRXMDtkh0MI87AGJMl6J7JgysjOEy6DgWi+8WILeklsYO++CQS8C4gGXjWikfe1F2EAHJIppYUw2KFi0QH0srUu/NzpNpVddJl2nYFDA0FLEWntkR+/qBezs0wlNSW5UHXm/uWFfvsZAuPbGKGkoxNYCsehme1c6lkHB3vqfcchfEJhSSDIX2Un3GydclWt8kLJp/CoDRR16CQD2BmvChh6XBP+qXBV7g927m2XBv645s2aLwIBQ1xcQIzPro8R0s1XdLcKlbx5y3dX1tUFJN0vGBzur7MuN7VovvRgWsHsaG0P7kX6+jJSwERhCmBuOXHQGHQooaIEY7kXmqNlhupCVw/24Vq0MrVUZifKb72nqsoXYtVBRxtCieI0J2ruiFoiUsxPrbu8J1n73VCvG9d2jUACBbrZ3TSPV3T9/WQYF++uQ6xJoYSN5v+leKCJGjP0WEDlVBEKisaUGB4BPTYmyeQAA51Z1nR6BScjOxo2NUDeUEKO57qCgscHYQ92M7Cg1O7d9U0Kghfl5v9S6NRpbUNLjXnxdzc47XiGEORyLbSTAOeRbuz/IcGhstf1ycoGOgnQKwkWmqVgbtUAlm2F26CXDKrvcPOibkqIFLFUwfTwkMRCjY+glh74FFAT/2s9FyTWNRDcD9uYSjLplPQVHs8NvHgZryrRRsnYp37/k60DR3hUd/wift6wF6o8YMZrURbOzorER6GYmfSZW9LiMlKiQHTsjGmsYlVH3iGgYArKTNUar+8ESEJhQsJ5mCP4D5rqOWNfEe8HCb4al9jIouNarLrYLWeK486ycK1oVtramt0EBY78F5KHb9aXQM/rQzcpNtaYpwVz3eJhha5cYTXUV2SEYjfQyKMjNQ9jX5SM7jnIdMNeN1zgNhdjW9IxDPpA0O0e6GYdGzB8K7bVLjPdFRmBK9FkCoszXAqVhCHe/aVrvp4XGtuTW6OhmwBbZSUiJRpgPeztogZIwX0MLS65pmyZqPoxr9hsKRg3ItDCFtmbe75p2ZwJBs5PqrpszrRbIGLiCBfei2bmsp6/1Yot3yN9OQntNFh3hJQMkzc5RC6Q9dOwnrL3CSllN6h45o4Z/loT5FK3KMVRUt99WjgbXWpZpULDX7IwEhLZuPa0X0AN7lEuPlJTsyDmI0fZ5xnBVBCJNK4SFOsoaCh1CkqlINff5UKxLGAqV3c14TRR7yFLK72EgUTbRzQqaHQ0tzCYtkOM2UcYOuJmyVkXXnB1za0yisanyhiwGU0aMGDlG6/vMUnKM7MH4wBKakhISpdYCvfi6mp13vFiH8b3b0kisyw6RTHVLL3G6dolwyE/76mGVfUR2CC5kpm4k0Ms9jqIFKk3c6Ynufa5D/GeGQcExVDQ2JT0QI30TVWyASVb6wKrZYbmQ7RrVBfHU0gTLzTrbtZKhvUt1J39EolQGBQXaKCcPp4/hSit0WGU9PSM7YdWUGIKV86LZWdHYElKi1OwAwLTaL5we2UEW/B8RIxUtLLnSrREYBt3M2BnZWTVRhOYhFBCjhW6mcXkbDvvNyM6l2bnWqy3mBBBYHw44h3z7qSaLnbVAfFckZl3ufq01MKbGne8TVsoIFS2GlTIQGLJ2qTTBTrVZBgUHIwGCgD7W3V4L1n4TmhpCmI0aSIiRT8MbziF/r13i0c1smS7J1uyQnr89tEvpubO+dykGBQVUkqIVrKCdgO5+MJlutkJ2Et1M0ZSYIWmB1k0JwTUt0e42TdQcpqmwci7R4wwjB6YQKpqtnBV0s9REbZASr0dgfMGogWER7c3R0vp2ITvXetU1ESZqwPEwwzqMp8liCLtDBznBmxH2CJQ0OyxXOrMRpDNd6baTRf1LvFyXd8gvIWcM9zh2onsxW8Z73AdOFs4xZ0ePlJQyh0aGQUFJv0Skm+2REtZ+c13ikAVY7gmGCxlQ/7yx8obSYmhV0r7KSKru93a3thhmzEBo+1nIF7RWGoOCuSkJrmAkoGgesqX1VNDsaJook5qopa4h1I25QK7clKjzhvbuZnqkJGuM1toahgbG2INRw5Bc1Mj7HQjapVdeV7PzjhftkL9zwmEd8vd5F6+uBTrW5dDj9sgOU7vE5qIDR+1SL1oY4/5t1lU1UWUXJ9ZnYq/ZmXwfwT9DW1OcYjONBNy2eaDRcv3+eca5Duk5yTIoGOxWC8Q0KOijBbKHzKVYl92c6d8X5c8EL6y0qJlUuWzOyM5Ks7NYOctpYRkx8ksTZQhWzsk9zq0MFRLdTLNfFA7jKQ9HhcAUmxLCId8cQ1sHr9+vLxgqDCzjA7OvO8HDZsrj57auZucdL+fIh4PsxsbmuMcHC2PyBbRchghIFPmQD3R0RepEU9kjZ1yDglJdggC5YKjAyOfYH5JYn4kyjY2DPPjdNaYhGn5LN2PQt9Z1mYf8dV023Tc9z5iH/FiPi3BVXSvp2kYSUt3B5a3kfki1nt6Za8S6CjpfomgV6GYaWtiCGBW0NRQa21pjpNd+mOGGwQRMExkpscfmwRIQo7F76igAACAASURBVGAHDMZt6zJoYTPdrNycaTVGC3LmfIh5Pubz1OsAV7Pzrhct1O4wCeU0UXtaDeuluE/wZtYtv2wJk9AOBgW1HA21y5vZHg4WtzvFS3z+0dLvTTMZL2l2ku5DQzezpbqO6EB2QHb0BgV1ZIejVUn3F4tmVR+ycBEuGo3NlJ9nbO0SL4/M9kF+Dzk7PEOFtRCbsV9jzFyXOxRqWmWrDApmWpgv0dg0bmxHzU7SwKhobAVkhyHMT1kwa6tshpEA7BGByeGaGmF+kRam1xiVLLgZdLOArfHB6DzumOCvZudar7iY2g9gfTjgHPKP7kUsF7I4Wcy5QCQtUNTsrF62hEM+UHBFYuVzVBAj9aFjKHPnVS/x+dBRoqlQjAR2TSqrcXA7K/IeVDNgRnbUVuTHRoqKGOXPG48uCRSMBNRhxpW66mHINm+IjUQdmjMCTZDtQhbrmsPnItbVIjvb5oxGaxz4tOeS0Qhjv4nGFlaH/OSapkFgkpPb1kiAEFaakJ1Vc8awXE77XbvHMQT/pkA3sySXt6O2hoDAFOrewgiHAVAM88LOqGHyAR/MBGc+T70OcDU773qxJmr7KTaPvpWscNl1t1ognnvR7mVLtJ7uoQWyFS1Qr0R3jnbpiERxtEBbJIqho4h1t02UOn2+sF8gGQnwkZ3Rewq9CFgOy/RDvuMe8vf0WRq9c3cdGFbDwBGd5GmXLHzAYShEaR7ILmTp58fdvRu/rr0f7BYxYmqBSkMhTbOTrKcL2hpNmGZCYII/Ijuq5iEhRivjA0NAHkp1OTS2ZHxQEubrLLhv8JvPBa/uQjfzmW6ma0rCDuFyLtZ15vPU6wBXs/Ou13II5R4OWIfb/eGLtd89dz4f8smaHQbykOoWXcjILk4smkovxKikXRqsjm6WJ6wroKRb4+A6IjsE1Giv2YlOiIxJfiV0mIzs8FzItnlDrEP+AYkiaSb3zzOmthHoQyNmh+0CCYHhI1E9tEDWGlhTQ84UBgVFK+ekrZEfcG1BW0NBYGwB2SFYOedcoE1dveAfcwhqMV9GQTcztkFjU9DjQjYSSDrBgDsmdVOyp8eNPtHYLmTnWi+4ePSt+XCwt4gmT8cZ4XPluhxaWK+cnYNrGu2waLcamE6udEx3voN4XouUzD++PyQxMqKAvRaIJ/b3pWaHMMlPteKfpPuhYk1PDzNmHZqryA5Lu8Sn5QIr7RLRoGBdj6YVNH1c3m4762nqUIgcVhrr7ui+TIMCfxT8a+hmpWaHEdJZDEElaGtyE7VuzhhISc4xWjVRFMH/7Uhjg8OEm4puttcYTd7jbpyabpYMCtbPyDscnKaRfPF1NTvveLEPHb1yKY512QnpXC1QWsvEku2a1tkViZ03RLzPpj3dTLlXY0whH4nRONTE/tyGJNem5OzEPx37kH/Q7LCRh2VqGetytUtsZMfvDBVoWiDyUOigXSJa07vAPeQDBUSZQDdLP7997vCQKLr1dA7/PCI7uuYh0djWh3w98mAzArOimxHCP0vGB0Mn2l0O01QhRjfcsbixhRBwDxOcUvAf7D3S1jLqGyhGAqnu+plzGRRc62UX3VLV7ZsHkssQe7K4Oxwsgmn94WCTd0Gqe0RKOIeOPWLE4s7vNTuT97BmOTzJ69rDRFhLEYx1j0gUDYEJ2/2ycnbW+/U+wAfOpBk4DhcYh9B1PZYL2f65wzrk7xtVJr0TKGiX1PcaNnXpQ6Gwe66TBP9h76LHoMe5NULbKb+HORQiN1E2HeRXyA5DmF+ytKYgMGlP66YkMBCjue7KqIESejnvaR2CasMED6PLlxluuGHaPHNuYDQ7N9wxbc5mNzh4JQLj7R13uPxsnHzcr7buK6+r2XnHK71sP9BdkcJcl4vs8OhmKNZlaZeWhHSO4P+AaOT9dkKi6AiXnhYGJHeobVOiPYgD5f3qne5KoYH6/RYT3TtZyLME3jl0eIc8vHoejj/UVe53dx1YSFRVE8W6vm6/3z7DJm3d+7DL9yIhO/fhiPzGulx63OSCeiiUD/m+ZLmsaHbmgE8/21gDa6REQY+bHeLCpnlIVs4KBGbWJ/lp2a8lhGmma+jHpe4NE5yWbjZ8wGBCbqIWupkSKRk+bBCYyQd8oCA7N9zNtGHH3I1DuJCda73iekuT0BuZOz8lWgKLbrYV3n64cQ6i+8MXvembX2Ta/R61QJzrsNfA8JrJXT6HD+ppOxB/b3tNlPYgAxyRKIZBQUkLNHYwEQD4LmTH5oGr2WG6hQEFrQqblkvXLu1od2ztElkLdAgrVecuYVuX2PwW6bMUZIePGN0KzZl6KDQ3HmZDC9ML/of7fa5VaqI04Z+xbljVpdDNUgO2as5uDJe3tN+5KYl0s1HflNi432mMdUeXmhKta1pEdsbVWeeOSa2tCfY+112eZR8uZOdar7oW62nuIf+NVNfuXra8sLzypFnfRG33+zbxmqj1S/GN1JztEY23Wfehp5ttXYbGyauvbax7bPoYdUsC5C5aIAKyU9QCkS3Z9583vpEAF4Fha+96hhkDa81OZ4SLnLvEo4VtDW1YtNz7XgNDMsqJmp3t8IZTd+8uqbemT/oZU2xK9AjMmsbGoJvluhsEhkGPmxGjNZ0PExysim6W67qPABLdTC/4zw3j3JxFutkEpwkqBYDhQzQOmDVczsf9ql3TZhpbRn3dRWO71gsvFqJR46LzOe5c7vy+OaMhO47b9N2s3WqBiIfbHs1DdHlb/vnNefU1AI5GDSOheQDK14Gx3yNipG+iSpqdNyLSt67NRBCBJXeKPlzYPR/YQ4v8PCPvt7t2iezyRtcu7ZtqMgIzkpCd257G5jgaxJJVNg3Zcdx8GTPMGT2+0JQoDrip2Vk3ZwNjv/e5KZm2yI4WgcnIzlzX+RDpXAS6GQC4dV04veDf3mBNwDRrokYX9xuUTVQYIrKTP8Pe4w6nowi++LqanXe8xml+eSlf4vsJYEY0lPS4/QTw48Q9fOUPar4OrMn4fKgj7zcdOt4mB2P0mp3j4YDXlPSoa/dI1MRrSvb7pSFGu/2yGur9RBjgDwFy8zDoMhlymCYZ+a1a03dCqtUaxGFLn/3YrUll0Wfn/ZKHWMcQat5QyO2aEk7do1U2Q4N4sMpmaBAzjW1tUMAQ5s+IkePSzRbEaFUXhCZq3pOZkZIQwtzs6A7jJmuM5uZhPuRrEZikMUoI1+hn1zRt8zBsNVHTvF9t3WA/bFzepuTyprnHXnxdzc47Xkw6FLA65LMOSVUaG+twsJ00s9ys9rkf/ENdRAg0YZpAvL5rBGac9MgDcERK3iaPL0jNw16zw2lK7CZUlNGUANjkiYQQ8EZoopLuw++uL9CxeVAOAazZ1Z1IzcNuCPCRRBs9avpIzcM+b4itbczDEDKyTtdMVoZN2v0OJmsIgHVTzdDWbOt+QRsKrRFawvM3HfI3NDaGML+gBSJoYJZmZ2V8EBLdTGHBPdf1834TUqIN01w0O5HGlg/5asRo6/I2sayck6HClOhxcb9aZAf2Fg0UUljpvF+NDfmrr6vZeceLdxjfIjCsQ9Lh8MVGSub3V7/DYj8tBeNlu0c0PjqvPoAC8VB34KL3aKIcqSmxWzrJG4set6K/MO3CrcEBiQL47of05iEdxnsdmmnPM8x1Yz1287AfhvCeZ9zhTQ2xZz3P9u8L7UBkPwx5mwLug1EPhfbau7fJq9kQS12ytnE+3GYq2PrvNWGa889aX6LH6TU7WxqbHoHJdec8nDfncTM8ZAeZFuY5GphMj0sGBR434+Ct4ncG5N+Nn2Jz9jbvNyjrhrnulJCoacJgQv7657iuZucdL9bLq5dmpzcXfT+x1L5s9/v9OHnKy/agMSK9bG/Wwq+1QKy6g9nQSd6cVze+QE2zwzp0cLU1wLxfMsoX69rOmh3uofmgZWMPF8haoGpIZ7frQGpSHb95ANZNakLsuU3qR9L1PRgJkIYhe+MDllZwb5VN0TY2aWyKqXua2O+aks33BMvsjA+cDxRtTUaMfDqMcxCYZMGd6r5NnoKU2ERjmxGjt3m/WqRknzc0Th4fGAhMupcSYjRbcZtLs3OtV1y9J4AsfYInv2z3h5meGgLWYfxYV/+ytYVJMw0pWQvzSfQ4u9fWECes64wS5zn0uHVdlo4NiOyOvRYIINIw50tMoy0NewSGLMwnIw9H7VKv68ClEbsdItdr2MQzrFiGLJahQRxiqDN7KDRYu0VgSPTZ45CF8PwdkkV0IV+Gguys3dgmTNp8Gbvd7+g8PhAE/6kpSYjRR+fwgaBVydqamXb3Nu9XXXdIOUYLEsWwcjY5F2ixtL5jUiMwe9qdG2OTprrHXnxdzc47Xql5YE0A2QLk/jSVpS7jZVuiqbAQAmBFqyHXZTdnJUtVRhN1oKmQaHclBIal2dl/JjjX4Zg3BLyulu2g2SEfmvd0KHZeFovOt78ObywtUEUryKIJHmlsrCZ1uc8YzcO9VJeB7HQyRtkjRpTmzCYaWwHZIWp2vA8z3YzjQoZ5jx+nRAtjISXLIf9GQHaGYWuo8DYlWhinKTFuRWOD05lKAItBQUKixjdYE/R17TZcNdHZcLuQnWu94GLTMw6aHVoeDpc7f6Sp8IT527qcl/geORunQENg1nVZk1BrCk0JGSkJIVB/b2yUD4iTZrcz7eihXWI3D72QEnbjl5Coid489Bmy9DcS6PP87ZW75FcaLs7nYr4fVkgUz62RP7wpukB2cGMbwgQPo8qXSbSlISS3sDmckiSgT1qgHHrJ0uz4pSlhuJuZ+0xjc9v9auva+xYpWehx2uuwtcrOdDNls5Poh9lQ4e0jpe4rr6vZeccruwGx8y4m8qGjV37E6lDHQh6AZb9vjtOUlBAYljUysD0sUpCSIR7GAx3hsgcqFJtOsnwmGIiRXShhpM9arNsnZPZoucxBfg90VLpmp5O2plfeUC8jgU6aqPUQ68NN7wI57JrUcQoUt8b7jib4kRRmfB/s5nnGqru3yn4jGhTcZupaCCsERvN72zVRMa+FkANTbHYcPCFME9giJXejR2BS82B2+9UiJWZnlf02OXwwTk8Ly9bT8feWmh193dRExSYn5QPZlMf0Ga6r2XnHi4/A9D90fCBYLpdyNHogMG8TK/TyqDFiaWDWdZmhogCQzuO862Do99ixbtKF6SxKl7oL9RDg7NcWrL0BfYN2aKpZyMPQZ2hRR6K49C2Wq2LtOvBd6chGAivDFQ6igU1dHrKT9sulsZUQLsrwZk9joxgUzM1DWFsuT3DgNA+JEpeQB0fKgTGBi8AkmtUe2dE2O0PO2Zm1QDQEJhkfzE1UbkpIRg1zE+VICIzJTdRcd0x1L+vpa73gYk+E+ZqdLRc95hBwDs1Ah5dXd80O16Cg5ErH1hg5H+ADrylhH2yBimsaAdnZaHYmjkMWUHalA/qFaarrZq0K2zWtotkhW96zDFcOmp1uRi4eN2vyMENed0/37TS8oWlgCkMhkgvkui5Tg7ilsTFyduIhdAgOwGI1rG5KbKKxbTUlasvlue4a2WFoYJa8obk5I9VNyM6y30DR1uzDVVkIzGKVvdXWGLVBQdJExeubcnwyze8zXFez847XGynMbR/u9zZ5DNbo3XUKk2aW+1asuzrkMw63+SXObR7KiBHvOqT3LW2/q0Md62ALxP36HbJDyQVaNQ8sMfq+LgvN2Ndd12YPLegW8p0QmKMWSIfK1YY3egRmP7zppImiPR8w1110Z0z6rNvR47Qr/d7GFT2OhcAAy+eBaf2ffmc0F8hdzs44BdwNI5xyRnbCFtlh0c3syqDgbiYEEj3OZOvpOfRSiTwk17SEwLyNDnfjdOYPWBAjc0BKlE1UopslY4mZdmaURgKpico0tjHR2C43tmu94OIJb+eXuFsf8okIDJnusEzq4j/TXooH7RJXW7Om1XA1Ox7eR8E/VbvkA9mFLCIlIQTaARRImh2ujgJYtEuxLs+g4OB2l4YWWqSkhjyQDvkHC3maBmbXRNGQnfme6BY6HF0gbzTrf6672T4smj1kYdNyU1Oy1nBxhhZHQxvKkGVllU1zgbTbZuejc7jDqQX/C2K0WCPfKWGaqYlaWyM7eJJWZfDb/WrDNLFzTZsmjuXyIW8oNzu6utmoYWcRnf97wpWasETnS8jOcL8MCq71gqufaw+3eWDTBw4ub6S6BwSG1DwUNUbk/Y6eiDwMCTEKVLrZQtdhu5vZAwLDdo9jNlF2pQUCllBGNc3KGlhz1KqoEaOaVqVb6DBpeDNfYhotd3cdWM1DKdS5V12qBmY9FCLScseVeyfHoCAhO8ughW2VTRsK7WhsOVdFjcDMTVQ2KOCEaSakxe7q6kMv56Yk9KmbaGwsBGbYGR9M00ipuw9BdXNdLQKzp935VPd20diu9YKrp+CfoU0o0eM4WpWtGxDrpVjU7BA54wnV6KEFYrqbbevym53J+/6aHTICw0JJ9nUBHvIQa9sj3UyLlOxzdsgukFmzQ3aBPLqm6a7DMW+IOwxZIzAseifAN3Ipuf6x3M22dflIFHvIAsSmj/VZgx3gYXDDfMhPdDM1ArNFjN5mupnaSGCPGJEE/0tYaaKbTbgZr9bW7POGprekgeGGoCZamBaB2ecN+bk5s1pkZ9fsJGtre+XsXOsVF0vwXxbQ9xHmc1628U92mGZJm8B92frZApVjYZxpKj5QD8ybwwHpALqu630/BGbR7Og/F+u8ISadb7A2a5cA7rWwlm+NXDcwYSHKW8c7uuHKnGulHQodcnZoFvK74Q2peej1PDvmDTlq3XTf0kJFh3R9PTV0eG2VTRuyGANnbriFMaNQX2CEY9HCVkjJB0wIWqvhXd035/EBI61uanam+ZCP25fKuqmJmulmE6tubJbMTlujbUpSfk8KbfW5KWE1UW9z3bjf4TIouNYrrrfJUQX/vlPzsGiBWCGSy0Qt1uW6mzkynWRN+2AdFIGOCIwpaXa47nHU5mHlmkbV1gx9aGwHZId6T1jsDQpYFsZ79IwVKsp2jzvkcJE1iOwhi+08vPGB+5w8Imek5/qwXN/J+egCyWz6NggMs66nDoUcbrjDxefv5PHBjPBWeQi1Kb8nGRQEfIERXtuU7NzY3sYRH4xD0NKhsgX3fMj/+Ffi17V1k6X1jET5t1RX2+xsEaPw9lX857uu7rDTGCE1UR90dRMSFeYmyrjU7HyTqu4rr6vZeceLra3ZTgCJBgXdLaJZTdSyX+8DJoa7Drb7zS9FMgLzkUiz6qfZWQ63CxLFEwqvES6WwLuHG1stZ4d1T/iwQ7mU18JaA2PWoZecg1065K+1H6/sAlnU7JDCaw91u2h2yIjRWgtE0cDYud6abqa/vvfCUIgdFv3mHK2us/eYgeMXZEfflFg4WAxYEJgv8IZAQowS7c7Nh3x1U5Jpd2wEZosY+THu12oRjX2zM6W6yqZk3pedNTuY6w7ausmC26W68frelE3UK6+r2XnHiy/479M8bLnofQT/VI3RSvDPdgNiu5ABe2SnE2JEpB+uESOOBeyqmaQjMItYGuij2XljolHW0AX/wBY965XDxc+fIgvzC3lDPTQ7rOahu+V9WA2FyM/1dI9xnusLjY35/E0ufKMPtBgIAPDmhpuJyM44xWZH3ZQAcOa+0NjGaUZgSM1DQmDm5gEDuymJCIyhNVFzUzLv12ivww7hCrmJ0iEl2eBgRmCSVfTwha5uDled94uE7FzNzrVecbGE+SVudx8uOisPZyv478FFZ04W14d89mEc2ApvtRklwLo58+T9rpCdDsYH60MSK29oLcIGiOhLJ81OyUGOV3dr5XwjIzAsQfrRVZFMyyUjyr2as1KOEScUd3muM2m5WQPjyAYmK0TujYgor2nPzM+aMzd8wBTpcY5EN5vr3jHFBjU3JZzm4TY3DzRkJ4egzk0UiRa2DyvNGhjtIX+XNxQch262b/owcehm2dJ6rmtS3Q8Xje1aL7jYHOzNZJGq2elV1+cXOZdmxX3Z5rquj5XzpnlgNJMlzQ4ZOXtj0vnW+80IDOcww3YgS3U3yA75njjql0jXYt5yQh5Ygv+834mfEwV0HN6Qh01rkw1q3cB1gSw9d5i5QL3oZmvXSmoTtUKMGHWdiTS2yQe8jW+4MzQwSM2Ow+j9SquirGsMJgxLU5KREq7xgWchMDv3OJDoZotV9lx3TE0JZ7/JNS1pa25fKLVA2T0uXl/rEk3wMii41gsu3mSxJPjnI0b0/J7AF44D8Tp0QWDC+iXOFfx/JOo+bsP6MJOaEuIklHxIWocRUhGuwcAnLRAZiXIlZIes2clNlGXtuVOu1Sp0mOv6Rx6yDMf9sg1MACadLw1Z/IyCky3kycOb9XOHaSSQ6GZrpJrenBFprt7ecIPD5AKmtxR6yWh27ribqAWiITBz3RuiZsmNLARm25QEFgKTQ1vjfhPdTI1o5Byj1JSkuiR6XEgIDFezY2bNjvUkTdQLr6vZecerhzUyMDcl5Ill+otbd+2CwxGOx7rLy5atBaLSrErIDpsWRjzM2M2EtcN+Qx/tkguBbiIwrUJFqTqClWbnzXncrMnXXbOGdV2SgQmwzUji0VE70WfNfnjDpZvxDVfinxN7uFCk+/Lqjo5Nn011A3UodF81fR+JQyyfkR2ftSqMpsTPNLbRBbisgdEfbp0ZslV2SCGd2mbHxmFVMlQI835ZCMzSPMx0M61Bgd26x5ks+Nc2UYlutkVgWAhXosdlo4LPGNlRxtFe6+tc9ETsNT2DfFjk0hKOnHH2oaMLYtQp/DOiBL3220Nj5Mnc+ZUAmWkta47NJIset+p1yPqwrWaHcZ/FultnOsb9EOuaXV3G0CL+uWiM+hmusIc3aSjERB7YQ4sSjY2iBRpW+6UaFPR5Tg4rGhsz58zbOz6YCZNbIzCEpsRGGpvzS1MCbfOAhBhFQ4UszCfQ40bcs1U2WE1Jco/b0djUlsu75iHTzUiIUXJjM46bN5SbqAvZudYrL7bg3yXBfwduN1PImg8zjouU9EratqvDDBfROAr+KS5OxUM+8/pyqVvr/XLDP9d1mdqwHbIzORiz/PdYtZlNyRqBYVkYp7rb0GE9/XCP7PQS/Me6XMOVLtbIoVde1oKUUBEYzzUSSDXGlfEB0yqbPcTyNtLCJh94WhWsECPn4ccUeslDjJwPi+UywdUrhqumZofVlGwRGGQNDInGNtPjMj2MhkSlpoSEwORmJzZnwzcAsnM1O+940WkUs40owHfBYU6+1pa1bOE4sA+fY2pVwOWirw4di/UpYb8F9yLuYdEv7mbk/JMeWqAetLukBQIW2pJW8B9rWyQ5EIsWFuuunOlIFvJAyhxa7L0Zh/HUM2YEhm3l7JLgn5tb48k017VBDPMwvlyHPnSzOBTqYTzThz4bw6IdrW5YNQ80wT+WpmRaNSVquhkWl7fR+ZXgX+/q5cwt5/dkFzK1BmaAh83GB6kpUefL7OlxLAQm0eMORgKcuqnZuX0DIDsXje0dL3YeDn2Sb44vL7Z7EfPlZQtIFBch8NQJqy00qezcj/ekMfJk+mFv1ykXAiwMRufxBRGBydlALlDuM2Cr2aEjRit7b0ZdY8xWu8QO6SQ3JRtaLjUU9+hS2MsghjlcGMlDi/uqLsB7D62tsplh0X645+YhNzta+hYAbz/gZhxG5xe6GQOBmZGo0YUlB4aE7AxwmJxfEBiCNfJkbhh80tawDApmQ4WMlKTmgRPaanNTkhAYTt2M7IS3zdc/x3UhO+90MQX/G40GMRytJBxn00l60DM2+yVb1nIRrvXvrXddLv2lx+FrW5cpSCfnfuxzYEjaD2BGYFJTQrIwBvZNCVcLtG6iWHXtjh7HtOjnIw9HZPKlBf9zjbeJq71LDopruhljv+mdM7q1ayVhv/O9+ub8YlDAuH/tB9zh8HHytHBKAAg25ve8TX5pSgjITjBL3UVbo9+vT3Wdz0gJpYnCLSM7mRamdbtLSEkga2CGiEfkpiQ3OxwkCi7u95ZyfD5jZOdqdt7pYtvVAntEg2h92oueQeZK90OMsKrL368nIw9JA5NC7QCS4L+oMSJqHjbW07z9Tq4P0peuwRvpMA7EQ2eiCLK0d7GuXfZLdGP7MJhcl4kYfTHv1/kAH0DRAn2xOtwyrYa/uC/NQx42ET4XX9yHXJeJGH252i/TSTBdh4+jp37ect2J26R+Md9THyeuQQGGSAv7OLmsrWEgO2FGjD5OHhhJ+TKIiFHab0JKbh8IltZ23u/oM31LTTfDbNQQonaJli9jLRxstsqmaWB2yM7gSfvdGR/cwpWzc60XW//4v/ff41/5sT9FFfwbY2ANPxxtWNEHergBsSeAqTl7m7iT0PvQq67pUjcdXEbyIT/XXR+SCHk4ecI6ce/fzSGU6Jj2xWq/APmQfxvwNjl+3bvNU3Fu3SHXZWqMvrgP8dBMRB6MMfhws/zD+Hw/fDW5rP1g0BpT3Y8Te7/pkO+omr71fpnDm/V+uXX77BfDB9yMx8dxWrQqDATGfsANDh8nB7ByYAAEe8fNRCRq0cAQkJ2ZHvdx8rATydIaETG6mdj0LZbLHFe6wU8IIayaEo5FdEKMboGF7Gwtre/hDW+4AwTd6KuuS7PzztZf/IWv8GGw3EkS4kP6bT2hIjy0P5QO+YyJ5a1P87Ce3DIPSV/OL9t1XcbhINX96NaCf8b17Xs4+Gpy3An2er/Mifv6MDN5WLNQeDTry/syEQaiNTKLvvXFzeKrXNfjwy/j1f2Ymyjufj9OHn7WnTHu36Wuox7yU92vVod8Dn1rhWgQacTLfeaodNTtIb8HUuKoQ6y839HnQRlzGPJx5N4PZj6Ivn18y00JA4ExQ6SFfRz9kgNDosclBGYJ0yTUNXfc8ZcjYsSib2G2yp4RLhpSgsVQYfJh0cCoEaN4RE+IUaababU1Kaw0aYHCiMl+wOer2LmQnXe30suW+ZIBVNP6xwAAIABJREFU4ovxq5GPPFgDfDWuDs3El/hX5AnrUtctzQPxpbjeb7+XLZlO0unQwXQv+nJFU2Fe3+39wERf0v2wIDC0w/g9Di2ihXyg1f1yRkqAWbNDRWAcRk9uSu528zzjNWfxOjDrGmNyc7YMhdjIQxqG8Oh87wUp2dDNqMOxY13G/WvmdPvp7auch8NoHjB8WGhsM6IxfEFAdoYPsYmaXEZ2WIjRByQEhucW5u191kQ5ngYGsyvdjHDRNDDGYMINdkaMbuENb/igR2B2iNGH8IbRfs6tztXsvLu1b0oYoWupznoSyqgbX+KRppImltyXIneSvz40U+sWkBLOhLXPSzzt9yvyfnPzsEZ2iIeOdVPNn2ATD/i3LbLzkUjf+nJV+21yFOQMWBCYEAKXbjYjUcymOtYd6AgBEJ8R7KY61h3odLObjcOm7XCBpwWiD0OKQxbGfpf3RfrMMd5vW5og7/2WkJ1x/LgKpyQIx4cPsCbg4/hGrruim6WmRCv4R9QY3YyLiJznuYUla+9NXa27GRZr74+jW6ycCft1K8vwe3jDaO7qmjkXyI8xxBgjnLmanWu90IovW9flZRubKJ42Ida1u0Mo4SU+WNys2e2316GZ+xJnTiy/zIgRecK62W+HJnXkTkI3h44p4D4YSmbNmgbEdExb/94AnlsYcPzdUbVAKzcr1rX44mYjskM0wgCWpoT5OQaWoRCzWV/qcmnEy7DJdRo2rQ0KuHRUKrJeQIxemXZn54P3NL6trJEZNLZ4wB3fPmYNjDqkMxbBfXZjG4gITJgNCt6co9ZN1t5vzq+snFmIUUSimBqY1Ox8nDw+hDdMDAQm0djCGPPNMHLqvvC6mp13tr68xaaEPrG8DRsuehLrq+veh83kNtmhMur+/+2debRU9ZXvP7vGO6NeGUREQCRiI9xGFJMYQeMTMGpiNBoHNKKm6XTal0S7VxtNt5pkLWPaLNuHWWoeJEHt2LZ2jG2emEliG6NGFAUTpGlF1MTIfIe6Nf/eH+ecqlOX4QJ3nzu5P2uxqDqnatev9q069fv+9vDTj5TsOhlXjZQUdNMPwxEC1XS+RG36lprd5G7sqoi+WpGqWZ/h2dWO7FQnX6DbcrlnvZX2mLtyftqDckRDU/xCSDxEkcYW+r6pRdZ7LGJp7bu0SzqfYsSoNj1ZMz0uojQ25cWm3S1i6YgdP40tFNnRSGOTQETlc5XWyBoF/xJPkZISuUIxFClR6OoVr3ZjS2jVwOA1akj6EaOE4v4y5ViCJP7CmCtQUIqUlGNeely2UCJJgaKGXT89LkGJTL5I2sSOMdhI+5GSbn9VuD6l+GNbKFdWm+tTff/xgurKbbYYhd3qeLV2Bk/FYzXpDqo/isUyOcXxpkMRgkjSScIiVT0CU0ak2llP065e1CG80lzWSwnr0aBAtwuZZ6e7UPLSHtQm496YO7K+2FFMjyuWHd15ve+FZzdOthD6HitGzrTTt6BaC1Sxq92oQXG8tenJ0aSNRlErqN1YYnfjVfkd8if0xXyuUgOTTPdd7AQRo0IhX7Grsq9K0FChkK/uW6Nht1JbU1a2W23trVYDQzUSlSuWSZDXESVA2W+okMmVVEVJya+J6swVSUuBkokdYzBR5/8oZv3JQX1SRzzU+ekOGd9ug5IoCVZuu7XHm/QiUdXx6jQWDFZCNf0QLkiviFQFP9SFcuezEdiNbLzFMtliWfWzAPiT25LaantdqLFErqAXJemZxqZa8J/oIUqUIzuBXc1Jfq1d3Roj7UYu1QYx+ulxUTSeCUSJZuMDqEaiNO0m4jHiMfHTGqNKI9ZMj9tN10rFNLZSqLZGFCIaQRpbMZ+tdiFTiGhU0uNyOdUaGBLV/Xsq6WbxvteruFCjhoRWDQzefkMJ/AiMK6gV/Afpce3ZgldbE9PZC6cscZIU6ch6kZ2ykt3BiomdIUZd0sud7/TTSeqVJ/nBBKxOaSKaTsZ7RKK07HorrIFdXXFWFVEaE/LK/hzFiERUeLyKdgORmkrEVCMw2UKJ7nxRTeyEx5vJl/SEb2gyE43dsr+7vVONeAJsz3iTA83vW41dteuDZ3dHt2c3SKHsu904pbKrpN2pXc+CxSb/uqPaIKZQrrT31k7ni6IFd01EYxDbrbT2Vq513X3tUt/txpOB2Mnp7dcCxJLViJGq3VD3OM0aGOJJP92sQNLlySlFYAh1j0spppu5eJo6yZPJexEYrYL/UixNmkIoAqMjzgqxOuokXxE7JYWmEoMZEztDjOrkwGttqDdh9ESJdmQn+PHSjuyk/RqjbsVJvme3VvSpiaiEn36oON5UPIb43ZYy+RKJmKjWwOR8/2oKSc+uN16tv1k4wtVdKClGjKorwtmC5ngDu9FEUgG2Z7zrQ4Ni5Nezqyuiqnb98aZ1BGVdRKIvaBATzSJLdTGkUckPQQRG/7oer1x3QDk9uai7OBY0tMkVQxkRGotCNdeHMomYqCwKxdONALhCphLZ0ahViacaKnbjJcXampQ33lI+Q8LpRYxI+nazGb8LmVKkJNlIAzly+RJJrYJ/oJSop4GcJ0rIq6WFFRMNNEiuKkqUIjCFeENlvCkKOEtjMwYTwarwDv9HXO/H1sud78zpiqi6ZE8RpTSZ6Vm7pD7eIHKmnM4XRM4UVrCD/TkC0acdKckWymQKJbUJc890Pk1BDdUIl97Eq9oqO6Mo+qp2q4sAelEjfzFE+fpQWWQJRIm63WjGu71L2W4iTtlBe7DYpD3JVxYPQdpzxo9waf5eRCailCPgnt1qZF1EJyIXjhhl8kW1v1k83QSAFDKqtTWxumbvRr5TdZNOSXnjdflOYuWCXgTGF1HlXAdxRVHikg3ExJHt7vLTwpREVKKRBrLszORVa2BKiQYaydLeXVBNNyvGG2ggS0d3jrQUKVtkxxhMBKvC27oK/n3dFeFgMqP3YxuRKIn4R7Hb30BRM10nEH31yTgxza50QURDyQfJIHe+6KWb1alNQKuRne58STUFE/zIjqYoCWpr8tGIs1wEojqwva0riGjopt5VIyW64mx7l7KI8se7Td1uEDHy7KpFYPzrWVdeV5Skk7Xps5oLAbkoxLqfnlz5XiimU+cKJbryRRpTCZXW9OGIUSZfolHJB4l6T5RIviu0b03fJ84xX0SRy1RbOSvUwMT8SBS5ThLlHEWlGhj88Ra6O0m6AiUlu84XUV2dO/0IjM4k36UaSUiZjs5OT5QoiYdyop4GsmzP5EmT14nG4UeiJMfOjk4ARCP1cBCj8+00ACiXvU33oqQhGSMdF0+Nx4X6pFDyN4zrC40pIR0X2n27dXEtuzGSMSGTy5OOC+nEgdsVEWJ+S+xKV7q8bk5+XTLO5o4c3fkiqXhMpQsZVMWZpiiBamFztlBWmyBV7PoiakS9zo9MXSiyk8nrRYzqaiIwerVAgd2dlZRRrahkbTofKKab9Vi00J7k71BOj9tFRClHEat2dYV1FLVLzlU/aw1a4w0az/iLTVoT8kqkRDmdL4hwaS8KBeMtlfXq48J2M/mimg/ifgQmVgyJHYWJaLwuiBh1Efe7kKUURF8s7Y23nOsioRiBifkRo2ymXTV9SwKx07GTlBTV0rfKSS9NsKOjnRRFnJJ4KCcbSUuR9s4MKSnp1ENRjRh1dnUBIMnhHdkxsaNAPp9n06ZNFAqFyF/rlNEF2j4xhvqkY+6YMcTa/8T6rj/32e78I+DkkWOoT8Kc0WPo3vIO67f1/UL4mclxzhxftbvl3TfZ1ocLbDKZZPz48TWRnZhSWgLUpjsEUTQVu8kYOzIF1XQzCEV28kU1wVex66fVHNaisPEcoW5LlciObgSmI1uk7PRTobZVop3K++xE0bgjslqVWlGimQ5Va1c3EqWeHpfoHzGpXYOYySlHEJPelgKZXJFkXFQ3t97WlVdNG4XqdT2H3t8MqhGjTL7EQQ06i0KBKEkUuoiX85QRYgoRmEQgogpdJMp5CokUGtP8eL033mJ3h6ooidV5oiSfaSdNnnK8RcUugYjq2kmaAt1KkZKgxqi7y4sYqdQtAc63m+vY7L+OnoiqlzyZrg4ARGOD2UGMiR0FNm3aRHNzM62trSrh8b2xuTPHn9uzNKYSdOWLTBnVRFKh/uPP7Vk2d+Yqdo8e3awS1fjjjizbMlW7Hzqs5YB95Jxj69atbNq0qbIS2p4tUJ+Mq/k9qNnx0pb0vh6eOMuprgBCNbLTXShxSKNegWFlkqQoSsCL7mTy3u7V2rU1OyJKsdoR0UQ8mjSg2oJ//UiJ9mS8x3jVRZSuKKmmEUfzmQhEX2NaWUR1F5QXhaqLTZrXyXCkRDcC4y3elMsukvF250uMHaE0WfQjD/FShng5TzGeVInABOlxLt+ptzklkPDFWSHbQYqiWq1K3I8Y5braSUuBspJ4CNL5chlPnGWU0s3ET+fLd+7wIjBKoiRIuyt2bAGq+zD1lSASVejcCkA8NbzT2Aa8ZkdEFonIGhEpisgXe5yLicj/EZH/EZENIvKFHudv9M/9j4h8vce5K0Xkv/1z94pIInTuLBFZ59t8RESaDnT85XKZQqFAa2sriUSCeDwe6b9EPI5IjLITRGLEFV9TJEYZz24yqWU3VrEbi8X75KNEIkFrayuFQoHGZDXXX2tyC0HhbZlMTnll0U+7yxb0JvlQjexoTzrCok8zEpVOxqv1GUp24zEhGZfQBDSaaIZ6l7eIas4gisL8nnYHeUOFqFpw7yKsoxGTai24Q2KyQalWxbOrn77l2a02tNFKuYNwLVA0EaOufJEGJYEaRB7ihS5STm9zyqDxQTnbSR15ykqiJIgYlZTtxv3ITiHbRZoCTkmUBHZz3Z2kyavsYQShRg2Zbd59pXQzCbrodXmiJKYlohKe3bJvNz7MIzsDLnaAVcAFwL/u5tylwLHAFOBE4O9F5BgAETkFuAiY7j9mgYjM889NBL4OnAxMBsYAV/rnmoClwKecc5OBPwE3HOjggxqdqCM6AUEKc7Hs/Ps6rxvYLZUdgqD1boLxFcsOjfTrwM/h9BftH1vwcudV08IqK6F6NSVQ28VJc7zpRIz27gJOMS0MvMiO9sQWvL/bDuVoRtBtKarieU9M6nf9g6GTxpbeJVKi2wyjEinRbqigHjHyx9uV161VCXW7057kg+eHKGpgNNNcw3b1xU6c7nxRt2bSX8kn30WzZMjFD3gtdrd2Xb6LJrrJJ3TsBhGjcq6TRummmNSxG6TzlbOdNJGl5Kdz9dmuHzGKd28jJSXKSuMNIkaJ7veBqkjpM6nAri920rp2k91exCieNrETKc65V5xzfwDKuzl9IXC3c67knNsGPAR8NnTuB865LudcDliGJ34Azgd+7Jz7s/PUyN2hcwuAF51z6/z73w2dG/QEk/1SRezo2g1EiZZ4C4soLWEGtbnzujUwEYmoZIx8sUyXdlpYMk5nrkix7NRz0bVTi3ra1eryBt7frdqBTMduzN+3KKoJfpSRnWpNiXYERrtbY3WSr2m3Mt4u/RoY8MarWasSthuJKFG3WxWT2hGYfNHbNFt7vEH3Q+3rerDnnVpkPUhjK2ZoIUMu0axqN1bookUyFJI6doOIUSzfSQsZikmd2ppknWcnmd/mpbGldOwGkZ2GnFcD4+pGRGIXJbtBxKgh59VmS/1BKnbx0+7qs544izco2R2kDLjY6YXxwFuh+xv9Y9rnDheRwe4LoPoHc3jiYU+ipKOjg6amJq666qpeba5cuZJf/+oXnl3nkANUUCtXrmTWrFk1x4LxOactdjxPaNZ+eHar9RS6tSqerXwxmq5poB0pialvnLiLXaWOU57deGTjrbbt1RUO4X1VtMVOp/K+KkGkRN+uN94u9X24vPF15Iq6tSohu6q1H0GTjZxurUrgh/ZsUTXdN9wUJIrrb0dW2b+JGB25oIGJrl3tZhVBoXu8mKFZMhSUIjCVCEGxi2YyFJXETiCi6go7vP1a0jp2g4hRS8GLPGiJkiDtbkTBj8DU6YiooHtcS8ETOzElURLzI1zBeBMNB6vYDbrSBXaTjTp2ByuRT/BF5L9EZMse/h2xDybCvZx7zpY1zu0REfmKiLwT/Ovs7NyXp0VKWNzsTTw8+OCDzJw5k0ceeYTexr1y5UpW/vIXIbt9H+fubGnaTYeiObo1JdWvRHR2NScd1TFqd2ML0K7ZCdBeYY3EbkJ/vCJexCgb2oFee7+sAO0GBQGarZHDRDFe7VqVqt1oPmeqkZLIxjvE7PbD9UFN9CVSFEjQSLcnSpQiGoEoOYgOT5SktMSONxkfLdsBKKe1IjC+Xbz0LZTsBiJqlG9X6pVElG83GK+W2In7EZhgvIlGHbvSw25qmIudyLuxOec+1oenbwImAL/z7x/pHwuf4wDOnRY6NwF41zm3Sxqdc+47wHeC++PGjetVJF31w9/x1tZMbw87II5sbeCOC9sq9/cmHpYuXcrXvvY17rnnHh566CEWLVrEzp07ufbaa3n++eeJxWIcf/zxXHPNNdx9992UymV+/otfcNqCszn/wos5edpctmzxVlQ6Oztpbm6u1CddeumlrFu3jnw+z/jx41m2bBmjRo3a7Tj2VZztL1H9KNZFMLmFnj+KemsM0Y03JM5Uu9KF7UblX92GFRW7EdRaZbS7sfUUJco1JQHaaWHea3ityXXsRjAJpcciQGTft8Et1qO1O3QWLTy71fFqitSs1NMqHaSkRElLlCTSlIgxRrwC+nJaZ5IfiKjArlb6VmD3MN+ulnhI+qJkjC/O4kp2E34L7mC8SSVREqQJBuPVEiVB7VIw3nSziZ2B5N+BvxKR/wBG4NXpzA+dWyIi3wWKwCLgRv/cI8AzInIL8D6wGHjQP7cCuEtEjvHrdr4QOjfoCYuHPa1Wvvbaa7z99tvMnz+fYrHIbbfdxqJFi/jSl75EU1MTr7zyCrFYjM2bNzNy5EgWL17Mth3tXHXdPwKw7b139zqGO+64g0MPPRSAW2+9lVtuuYUlS5bs9rE1kR3F0E5NRCOiFcCoJklRpL94dqMa7+CP7EQpSip2lf2b9fdHAv10s4AoRFTCr2XSYKh9L4Zc5KFf/BBNpFo73SwginQ+0PVvLlbP6LI3CXVKEQ1E6Jb6kChRsuu3MA4iOzFlsVOxqxYpqY1EJZRqVZL1LTV2k0064iFREWe6omRXP5jYiRQRuRS4FTgY+KSI/ANwtnPuZeA+4ARgvf/wb/vNDHDOrRSRh4A1/rkHnXMr/HNviMg/Ab/BS9X7FV4HNpxzHSJyFfCo3456DXC51vv5v5efoGVqtwSTI9hzZGfp0qVcdtllxONxPvGJT7B48WL+8Ic/8Pjjj7Nq1SpiMe8CPXLkyOqT9iPd7IEHHuC+++4jl8vR3d3NmDFj9vjY2sjO3u3uDzU/tspd0wJU080S0UzG6yKKaEQ13v6wOyREX4/Ijl7Bf9UPIrUisC9EF3kYap+z0OdB8frQHxFa7db0VbuD/3tcG4GJKmKk599crJ7DxSstVhM7QE7qGStexoZoiZJYjG7qOAzdCExQuxTY1UrfqkSMgvQtJVESRIyC8dap2W2qsZtUEmfxutrxUje8GxQMuNhxzt0P3L+HcyXgb/by3FuAW/Zw7nvA9/Zw7jHgsf0e7CCgRjzsRj0UCgXuv/9+kskkP/rRjwDIZDIsW7Zs73ZDt5PJJKVSqXI/m81Wbj/zzDMsWbKEZ599lpEjR/LYY49xyy27/RN4Y6wRUXpqp64fVlhV082iSn8ZghGNKOxGlbYU5eQrVyiTqTRr0LGdiHupYKWyU99sN2AoRB76Y7yRfY9VFy364bozFNL5hpg4y8eqbYC1akoAcrG6St/bWIOe3azUUY83T1ATJfEEOVKkxevUmFITO/4+RuL8u1qREr/bXWC38RAVu0lflAR2RUmUBN3jArtq6YeDlCHRgcyoEuulBuYnP/kJkyZN4t1332Xjxo1s3LiR3/zmNyxfvpxzzjmHb3/725TL3tVu82ava0hLSwvt7e0VG6NHj6ZYLPL6668DsHz58sq57du309LSwiGHHEI+n+eee+7Z63hr0+4O4A3vgajS2OoiWqkbajVG/bNyG01tTXS1Vsqbtvrd2FLxGAmlFsZQ9fFQ+Lv1T+3H4P8e1zYoGAoRjWhqVaLyQ90Q+zwU4tW9VGKKK+75WNWuVq0KeJGoAK3IA0C3VDfQ1BIlJFIUqf6t0lqF+anafYBiWulxDT1qtpRESRCJAsiTBKXNSgcrJnaGGNJLutnSpUu55JJLao5NmzaNsWPHMmfOHDKZDNOmTaOtrY2vfvWrAJx77rm8vGoVF8z7GHffcRupZJI777yTBQsWcMopp5DL5Sq2FixYwOTJkznmmGOYN28ebW1t7I2oIju1aWzRpGfo7t8Tjd2htsLaH6JE9/MQTbpkJbKT123dC9XPmqbdIGIE2t+LoVWYH1mNUWQRjegjRtFF+qKqBYoqYqQ33mJI7CQUIzD5eFWUqEVKqBU7aqIEyIXETrpJJ1IC0E3VbkJL7CTqKYdzZLS60qWrrcc1RUkqJKK6RKm9+SBmwNPYjP2jt8jOE088sdvnvfzyywBcdtllu5ybOHEiL7y4inXvedEdiQlXXHEFV1xxReUx119/PQCJRIJ/+7d/q3n+N7/5TQDmzp3Liy++WHMuqm5stelQ+qvint0hIB6iqgWKSpyFJkm6rbL7o8ubrt1gp3jNyZdn24/sKIq+wK72DvSp+NBacR9q3cL6p1uj2dW2W0xUxYNaWhi1ESPNfVXCaXd1iqIkF6uvpN1pirOs1NPsurw7WjVRsRjdpGkk66XfaUVKQhGjLmkkpWOVVH31fXfHGxne7QkssjPkCEdKNNPCotoPJyq79TViR2+S1FKfrNzWnBwMObt1VZ9qTkIPimi8BzVUfwI0Jx0HN1bHq7UxJcAhTSnaswVeeWenemTn0KY0AB3Zgqrd0S3ej3ehvE/blO0TiXiM1kbvb6d5fWgKfX577uXTF1p934Lu53fsQdWJkeb37YhDqpNbzfFOGlldCda0e1TIbmNa0e6osF09/x49uro6rjne+qZqNEdzkp8Oreanm/TsJkIpUZotjIONOj3Deo0ayn4HuZzTTd8KInKdNPTyyP0gFicn3nWnK6YXgQlqjAC6Fe0OVkzsDDFEhMMPqqelLlkzwesriXiMw0bU05RO0FKX7P0J+0gqHmNMSx31ybjqj8yHxjTzV3MmMXZEHX95hN5Fe9aRB3PezHGk4jEmtDb2/oR9ZM6UkZw0yVvxGntQfS+P3nfmTxvDuIM9e6Oa9S7a584cV5l8hif8feWi2eMrt5vr9D4Piz46sXJbMxK1eM5Rldtaxf4Af3vaZBK+g5sUvxcAXzr9aAC2dOVV7X7x1MkAvLNNdx+xKz46AYA3t3Sp2UzGY1w4y9uz+s2tenab0gk+PKkVgLe36/mhtSnN4f51YXNHrpdH7zthsdOZK+7lkfvHh8ZUJ7fFkp74Pf7I6kQ5EdObnnxs8qGV25rXh9OnVveX04ykHn7k5MptzfStUeOOrtyub25Vs9sypjpetS5vQGpU9fqrKUpcqzfeOKVeHrl/dI/wxpt3ulPrnU2e3Z1Fxd+KRIrNdRMA2F4a3vU6YGlsQ5LWpnTNCqMWI5vTjGzWtSsijGqpY1SL7pcpHhOuXzCV6xdMVbUbiwm3XzCDb513nGrReCoR40dXn8TO7oKqSG1IJVh53Vze2d7N+Fa91aQR9UnW3DSP/9ncqSqiDm1K87sbTue/3++gWVFUjxlRx6+uncNbWzNqG1MCHNnayCN//RG2dOpNQAGOGdPCvZfN4sm17zHvL/bcuv1A+PjU0dz4ialMHqW7WnfuXx7O+j938OGj9CZJAFefMok3t2Q4p22sqt0bzprK+x1ZFp08sfcH7we3XzCDv75/FQtPmqBq956Fx/NX963i7BmHqdr9/udO4Mof/o6PTR7Z+4P3g69/ahpfe3QtMxQXm0SEK0+eyNJn3mT8IXrXs0Q8xqkfGslTr2+uRD41aEglOOKQet7e1s2IBr3rWcPMC+GZWwG9FsYALSd8Fl66C4C4Ype3Q2ZfCOv8proJPf8efMKFsFG/cW7LzPPgp78iIbvsJd8n0sd9Cn79u8pGnVqUppwJq37PVN5Stds+7lRGbvg+o4p731txOCDO6a3KDHfGjRvn3nnnnZpjpVKJ9evXM2XKFOJx3XQUY1fM34ZhGPuHc041MhmQLZRUa+/AG+uWzrz6wluxVObt7d1MPFQvYg9exOytrV38xVjl1r03+fZufF9PQDgHN/vi9KadOjYBymW45WB9u8UcfGOUvt3uHfCtI/Xttv8RvjNV3W75vdeI3f0RivF6El97T81uft3PSD34GTKN42j4u9fU7A4EIvKuc27cns5bZMcwDMMwhjFRCB3QbTISICLqQge86I620AEvtVFd6AB8aS1se0M1UoIIfOF56PiTnk2AWAyu+iXkO3XtJtKw8FGIKX/O6g+CC+6DBr0UQQBaxsInvwutk3t/7H4QG30sLLiNxLhZqnZTU06Hj/8jDUefoWp3MGKRnf3AIjsDj/nbMAzDMAzDCOgtsmMNCoYhEyZM4JhjjqGtrY1jjz2Wu+66q0/2Vq5cyaxZ3orCiy++uMs+PrvjpptuIp8/sALpuXPn8vjjjx/Qcw3DMAzDMAwjwMTOMOXhhx9m9erVPPnkk9xwww28+uqrlXPlcply+cAK82bNmsUDDzzQ6+NuvvnmAxY7hmEYhmEYhqGBiZ1hzhFHHMGUKVO4+OKLWbhwIZ/+9Kdpa2vjT3/6E08++SQnn3wyxx9/PLNnz+bpp5+uPO/GG29k8uTJzJkzpybKEo7yAPz0pz/lhBNOYMaMGbS1tfH888+zePFiAD7ykY/Q1tbG+++/T0dHB1dffTUnnngi06dPZ/HixRQK3j4gv//975k9ezYzZ84pxQq6AAANjklEQVTkkksuIZvN9pN3DMMwDMMwjOGMNSjQ5l8/C9vfjMb2wRPh4gf36ylr1qxh3bp1nH322Tz11FO89NJLjBo1ijfeeIObb76ZFStW0NLSwoYNG5gzZw4bN25kxYoVPPbYY6xevZr6+nrOPffc3dpev349V155JU8//TRTpkyhUCiQyWS4++67ueeee3j22WdpavLa337+85/nlFNO4Xvf+x7OOa6++mqWLFnCl7/8ZRYuXMg111zD5ZdfznPPPcdHP/rRPrvKMAzDMAzDMEzsDFPOP/986urqaGhoYNmyZaxdu5bm5mZGjfLaOK5YsYINGzZwyimn1Dzv7bff5qmnnuLCCy+sCJVFixbxjW98Y5fX+PnPf86ZZ57JlClTAEgmk4wYsfuuNI8++ijPPfcct99+OwDd3d2kUina29tZu3YtCxcuBOCkk07iuOOO03GCYRiGYRiG8YHGxI42+xl5iYqHH36YadOmVe6vXbu2Il7A28tg/vz5LF++fJfnRtGhzznHo48+yqRJk2qOt7e3R9YW1TAMwzAMw/hgYzU7H1DOOOMMVqxYwdq1ayvHXnjhBQA+/vGP89BDD9HV1UWpVOIHP/jBbm3MmzePJ554gvXr1wNQKBTYudPbSKu5ublyG+Ccc87h1ltvpVgsArB9+3Y2bNhAS0sL06ZNqzQ9eOGFF1izZo36+zUMwzAMwzA+eJjY+YBy9NFHc//993PVVVcxY8YMpk6dyr/8y78AcNZZZ3HWWWcxY8YMTjvtNKZPn75bG5MnT2bp0qVcdNFFTJ8+nRNPPJHXX38dgGuvvZbTTjut0qDgjjvuIJFI0NbWxvTp0zn99NPZuHEjAMuXL2fJkiXMnDmTe++9l9mzZ/eLDwzDMAzDMIzhjW0quh/YpqIDj/nbMAzDMAzDCLBNRQ3DMAzDMAzD+EBiYscwDMMwDMMwjGGJiR3DMAzDMAzDMIYlJnb6SNA22Wqf+ofAz9au2jAMwzAMw+gN22enj8RiMZLJJFu3bqW1tdUm4RHinGPr1q0kk0liMdPphmEYhmEYxt4xsaPA+PHj2bRpE9u2bRvooQx7kskk48ePH+hhGIZhGIZhGEMAEzsKpFIpJk+eTLlctnS2CBERi+gYhmEYhmEY+4yJHUVsIm4YhmEYhmEYgwebnRuGYRiGYRiGMSwxsWMYhmEYhmEYxrDExI5hGIZhGIZhGMMSsYL6fUdEcsDmgR4H0AR0DvQgPiCYr/sX83f/Yb7uX8zf/Yf5uv8wX/cv5u/dM9I5l97TSRM7QxARecc5N26gx/FBwHzdv5i/+w/zdf9i/u4/zNf9h/m6fzF/HxiWxmYYhmEYhmEYxrDExI5hGIZhGIZhGMMSEztDk+8M9AA+QJiv+xfzd/9hvu5fzN/9h/m6/zBf9y/m7wPAanYMwzAMwzAMwxiWWGTHMAzDMAzDMIxhiYkdwzAMwzAMwzCGJSZ2hhAicrSIPCsi60XkBRE5dqDHNJQRkTtFZKOIOBGZFjo+SkRWiMh/i8haETk5dK5BRH4kIhv8v8OnB2b0QwsRqRORR32frfb9O8E/Z/5WRkR+JiKv+r7+LxFp84+bryNCRP4pfC0xX0eDf81e53+2V4vIhf5x87cyIpIWkSW+T18Tkfv94+ZrZUTkoNBnerXvu6KIHGL+7juJgR6AsV/cA9zrnPuBiJwPLAU+PMBjGso8DNwGPNPj+K3Ac865+SJyAvCwiBzlnCsC1wE559xkEZkI/FZEnnLObe/foQ9J7gWecM45Efmif/8MzN9RcIFzbgeAiHwKWAbMxHwdCSIyEzgJ2BQ6bL6OjvOdc2t7HDN/63MrUAam+Nftw0LHzdeK+NfrtuC+iFwHzHHObRORZZi/+4Zzzv4NgX/AKGAHkPDvC/AeMGGgxzbU/wEbgWmh+514u/EG918A5vq3XwNOCJ17CPjcQL+HofYPmAVsMH/3i68vB140X0fm3zTwW2Bi+Fpivo7M3zXX69Bx87eunxv9OUeT+XpA/P8a8Cnzt84/i+wMHY4A/ug8JY9zzonIJmA83sXfUEBEWoGYc25z6PBGPD/j///WHs4Z+841wH+av6NDRJYDp/p355uvI+MW4H7n3JsiAth1pB94QERiwPPA9XjRB/O3LkcBW4EbReR0oBu4CViN+TpSROTDQCvwuF1LdLCanaFFzz7hMiCjGP705me3l3NGL4jIV4GjgRv8Q+bvCHDOXeacOwK4Efh2cLjHw8zXfcCflJwAfHc3p83X0XCKc24GXlrmVuCH/nHzty5JYBLwe+fcLOCLwIN45Q/m62hZBCwPFrcxf/cZEztDh7eBcSKSABBvCfEIanPEjT7inNsKICIjQ4ePpOrnTcCEPZwzesHPQ/40sMA5lzF/R49z7odUIzzma13mAMcAb4rIRmAc8CRwIpivo8A5t8n/vwDcAXzMriOR8BZexOwBAOfcK8CbwFQwX0eFiDQCF+LVWdqcRAkTO0ME59z7wMvApf6h84CNzrmNAzao4cu/A38D4BcDjqHaxCB8biLeZOexARjjkENEvgJcBPwv5xfP+5i/FRGRFhEZG7p/Lt4K+DbM16o45251zo11zk1wzk0A3gHmOeeewHytjog0ishBoUMX4f0ugvlbFefcFuCXwDwAETkSry7tdczXUfIZ4FXn3LrQMfN3HxG/oMkYAojIh4Af4OVytgOXO+deG9BBDWFE5C7gk3gXji1Ap/M6mowG7sO7sOeBLzjnfu0/pxFvxeV4vFWvrzrnHh6I8Q8lRGQcXnTyDaDDP5xzzs02f+siIkcAjwD1eD7bDFznnFttvo4WP7pzlnNurflaHxGZhPfZjuOl67wB/G/n3Ebztz6+v5fhzTlKwM3OuR+br6NDRP4LWOac+37omPm7j5jYMQzDMAzDMAxjWGJpbIZhGIZhGIZhDEtM7BiGYRiGYRiGMSwxsWMYhmEYhmEYxrDExI5hGIZhGIZhGMMSEzuGYRiGYRiGYQxLTOwYhmEYe0VENorIumBTY//YiyIyN8LXXCoir4nIj/fjORNEZEtUYzpQRGSuiJwRuj9WRJ4ayDEZhmF8UDCxYxiGYewLaeDK/nghf1+JzwDHOefO7Y/X7A0RiYnIgf5mzgUqYsc590fn3KkqAzMMwzD2iokdwzAMY1/4J+BrItLQ84SIjBaRH4vIGhFZKyKf3xeDIrLQf86rIvJTETlcRA4CngIagJdE5B9287xZIvJb/3kviMhHe5z/ZxF53o8MneYfGykiPwu9XnjTvut8Oy+JyP/zN2ZFRG4SkftE5D+A1cBCEfnP0PNERN4UkekiMkZEnhKRVf7r3umfbwMWA5eJyGoR+ceeESgRme+/9qsi8msROdY/Ptd/zndF5BXf7qze3o9hGIZRJdH7QwzDMAyDl4CngS8D3+xx7k5gnXPuXBEZBawSkdXOuRf2ZExEpgHfBo53zr0rIjcA9zrnPiEiZwIvOufadvO8FPAfwNXOuSdF5GTgYRGZ7D+kFVjjnLtORE4CHhWRo4BLgY3OuTN8O4f4/18MTAE+7JwrichCYAnwSd/eqcBM59z7IlIP3C4iY5xz7/nntjnnXhWROuBs51yniMSBnwDnOeceFpG7gSbn3HX+a04IvZ9RwP3Aqc65NSJyCfAQMM1/yF8AVznnviAii33fz9vT+zEMwzBqsciOYRiGsa/cCHxJRFp7HD8duAvAOfc+nhj5eC+2TgUed86969//LnCaiEgvz/sQkHfOPem/3jPA+8B0/3weuM8/9xzwHjADeA6YLyK3i8g5QJf/+E/5418lIquBvweODL3e4/57wjnXDTyCJzQAPgcEEZUY8C0ReQV4GZgF7CLWdsNsYLVzbo3/Gg8A40TkMP/86865F/3bvwWO8m/v6f0YhmEYIUzsGIZhGPuEc+4N4Ed4omeX073c74n0eExvj9/T8/bl+c4591s88fE8cB7wOz8CI8A3nHNt/r/jekSUOnvY+j7wORFpAc4C/tU//hW8qNJs59x0/3idwvvJho6V8DMy9vJ+DMMwjBAmdgzDMIz94et4kY2xoWO/AD4PXi0JcC7wq17s/BI4U0TG+PcXA790zvUmetYB6VAtzkeAUcAa/3wKuMQ/dyIwBnhVRCYCnc65h4C/xUtdawIeA74QSmtLishf7unF/WhRDLgN+Llzbpt/6mDgPedcNtRgIaAdGLEHk78F2kRkqv/6nwXe8dPk9she3o9hGIYRwmp2DMMwjH3GObdZRO4Ebgkdvga4W0RexRMC3wzqdfzUsDOdc3/sYec1Ebke+JmfufY2vmDq5fXzInIecKeINOJFPj7jnOvyhdZWYLKIPI83+b/YPzcX+IqIlIA48HfOuZ3AfX5a3koRcXi/i0vxUtH2xPfxxM6C0LE7gX/33++7eAIw4Md4zQ1W46X4LQ+9n81+ndADfmRmB3BBb37A6/C2u/djGIZhhJDeF9EMwzAMwzAMwzCGHpbGZhiGYRiGYRjGsMTEjmEYhmEYhmEYwxITO4ZhGIZhGIZhDEtM7BiGYRiGYRiGMSwxsWMYhmEYhmEYxrDExI5hGIZhGIZhGMMSEzuGYRiGYRiGYQxLTOwYhmEYhmEYhjEsMbFjGIZhGIZhGMaw5P8DN3VDLCtIbPYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 960x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "figure(figsize=(12, 8), dpi=80)\n",
    "\n",
    "\n",
    "# plot baseline and predictions\n",
    "#plt.plot(scaler.inverse_transform(data))\n",
    "plt.plot(data, label=\"Actual\")\n",
    "#plt.plot(trainPredictPlot, label=\"\")\n",
    "plt.plot(testPredictPlot, label=\"Predicted\")\n",
    "plt.xlabel(\"No. of observations\")\n",
    "plt.ylabel(\"Postion (x-coordinates)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
